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PubMed2026

Effectiveness of Caregiver-Mediated Spoken Language Interventions for Children Under Five at Risk of Developmental Language Disorder: A Systematic Review and Meta-Analysis.

BACKGROUND AND AIMS: Caregiver-mediated interventions are commonly used by Speech and Language Therapists to support early language development. Developmental Language Disorder (DLD) is associated with reduced quality of life throughout the lifespan. Understanding factors that predict intervention success is essential for developing appropriate, cost-effective therapy provision for the approximately 12% of preschool children who present with early markers for Developmental Language Disorder (DLD). This systematic review and meta-analysis examined the effectiveness of caregiver-mediated spoken language interventions for under-fives at risk of DLD, and factors influencing intervention effectiveness. METHODS: A systematic review following PRISMA guidelines was conducted. Five electronic databases were searched to identify experimental studies comparing caregiver-mediated spoken language interventions to control conditions in under-fives presenting with risk factors for DLD. Risk factors included prematurity, socioeconomic factors, caregiver language development concerns, and formal or informal language screening or assessment scores. Twenty-six experimental studies with 1407 child participants were included in qualitative synthesis. Meta-analysis was performed on nine Randomised Controlled Trials involving 947 children. RESULTS: Effectiveness was examined for outcomes including child language gains, child wellbeing, inclusion and attainment. Meta-analysis indicated a significant effect of caregiver-mediated spoken language interventions on language outcomes compared to treatment-as-usual, non-language intervention or waitlist control conditions. Non-language outcomes were evaluated via qualitative synthesis. Interventions significantly improved language development trajectories for under-fives presenting with risk factors or early markers for DLD. CONCLUSION AND IMPLICATIONS: This review contributes to the growing evidence base demonstrating that caregiver-mediated interventions can positively impact language development and wellbeing outcomes for children under five at risk of DLD. These findings support the implementation of caregiver-mediated environmental language interventions in clinical practice to maximise accessibility and cost-effectiveness while delivering optimal outcomes for vulnerable populations. WHAT THIS PAPER ADDS: What is already known on this subject Previous research on caregiver-mediated spoken language interventions has highlighted gaps in the evidence regarding the impact of risk factors, demographic characteristics, dosage and intervention components on child language outcomes. Developmental Language Disorder has relatively high population prevalence, estimated at 7%. Prevalence is associated with risk factors including low household socioeconomic status (SES), prematurity and late language emergence. In contrast to its prevalence, there is low public and professional awareness of DLD and a low diagnostic rate. Therefore, a strengthened evidence base and additional insights into the factors affecting success of family-based interventions is important in order to increase the effectiveness of service provision and care planning for this underserved population. Timely and effective intervention with young children presenting with early markers for DLD has the potential to offer lifelong improvement to their wellbeing, inclusion and attainment outcomes. Recent systematic reviews of the effectiveness of caregiver-mediated language interventions had differences in population age range and diagnostic inclusion criteria. What this paper adds to existing knowledge Our review examines the effectiveness of caregiver-mediated early spoken language interventions on child language, attainment and wellbeing, and on caregiver self-efficacy and adherence to language support strategies. Our population was children under five presenting with risk factors for Developmental Language Disorder, in the absence of other neurodevelopmental or genetic conditions such as intellectual disability or autism. This review adds depth and detail to the evidence base supporting the effectiveness of caregiver-mediated spoken language interventions in improving outcomes for this population of young children, and factors that influence their success. What are the potential or actual clinical implications of this work? The high prevalence of Developmental Language Disorder, estimated at around 7% of the population, and the strong association with risk factors including low SES, prematurity and late language emergence, coupled with the low awareness of DLD and low diagnostic rate, mean that a strengthened evidence base and additional insights into the factors affecting success of family-based interventions can increase the effectiveness of service provision and care planning for this population. Timely and effective intervention in this group of young children has the potential to improve wellbeing and attainment outcomes across the lifespan. This review contributes to our understanding of how to implement cost-effective, socially valid and maximally engaging partnership working with families of young children at risk for DLD.

Humans↗

PubMed2026

Novel Proactive Speech-Language Intervention Is More Effective Than Usual Care: Randomized Controlled Trial of Babble Boot Camp for Infants With Classic Galactosemia.

PURPOSE: Speech and language disorders cannot be diagnosed and treated until children are approximately 2-4 years old. To investigate whether these disorders can be prevented, we developed and trialed Babble Boot Camp (BBC), the first proactive sustained intervention starting with precursor skills including cooing and babbling. METHOD: Participants were two randomly assigned groups of 22 infants with classic galactosemia, a metabolic disease with known risks for severe speech and language disorders. One group started BBC at under 6 months of age, and the other started at 15 months of age, both completing BBC at 24 months of age. Coached by a speech-language pathologist in weekly telehealth sessions, caregivers implemented BBC activities and routines daily at home. A typical control group and a group of children with classic galactosemia who received usual care participated as well. All children completed standardized assessments of speech and language at postintervention. RESULTS: Assessment scores showed that BBC was more effective than usual care for both intervention groups. Greatest benefits were seen in the group that started at or before 6 months of age, with a proportion of clinically concerning scores equal to that in the typically developing peers. No effects of sex, genotype, or milk consumption were evident in the outcomes. CONCLUSIONS: Findings motivate a paradigm shift from deficit-based to proactive approaches for infants with classic galactosemia. BBC is extensible to many other disorders, with trials currently underway for infants with Down syndrome and infants born preterm.

Humans↗

PubMed2026

Characterizing Caregiver-Child Interactions Through a Transactional Lens: A Baseline Analysis of a Caregiver-Implemented Intervention.

PURPOSE: This study was motivated by the transactional model of development and examined the reciprocal influences that children and caregivers have on caregiver-child interactions (CCXs) prior to a caregiver-implemented intervention. We tested whether child communication characteristics were associated with caregiver strategy use and whether these strategies, in turn, influenced children's communication to understand how caregivers and children mutually shaped the language learning environment. METHOD: Caregiver-child dyads (N = 105) were participants in a randomized controlled trial. CCXs were collected when children were approximately 30 months of age, transcribed, and coded for four caregiver language facilitation strategies and child communication variables. RESULTS: Least Absolute Shrinkage and Selection Operator regression and postselection inference indicated that child communication characteristics in CCXs were associated with both the frequency and type of strategies caregivers used. Children's overall communication acts were significantly associated with caregiver use of vocabulary strategies, whereas children's vocabulary diversity was significantly associated with caregiver use of sentence strategies. Mixed-effects logistic regression demonstrated that all four caregiver strategies significantly increased the likelihood of spontaneous lexical overlap in subsequent child turns. CONCLUSIONS: Prior to the intervention, caregivers and children reciprocally shaped the language environment. This supports a transactional perspective and warrants further consideration of reciprocal influences when assessing the impact of caregiver-implemented interventions. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.32995796.

Humans↗

PubMed2026

A bimodal large language model reduces misalignment in patient education: A double-blinded randomized trial.

BACKGROUND: Effective patient education requires accurate communication aligned with patients' emotional and semantical needs. Text-based large language models (LLMs) lack access to non-verbal cues, which may contribute to misaligned responses. METHODS: We evaluated emotional and semantic misalignment in a text-based LLM using 64,200 utterances from 16,583 patient education cases across six departments and three centers. Dolphin was developed integrating text and audio cues and evaluated through emotion recognition, semantic consistency assessment, branch-level ablations, and a double-blinded randomized trial against a matched text-based LLM comparator (Chinese Clinical Trial Registry: (ChiCTR2500095933). FINDINGS: The text-based LLM showed emotional misalignment in 36.7% of responses and semantic misalignment in 28.3% of cases, with higher misalignment under greater burden. Dolphin outperformed the text-based LLM in emotion recognition accuracy (0.886 vs. 0.713) and semantic consistency (84.9% vs. 82.1%; both adjusted p < 0.001). Ablations supported contribution of audio branches. Dolphin received higher expert ratings than the text-based LLM and human educators (all p < 0.001). In 555 patients, Dolphin was associated with greater patient satisfaction (98.6% vs. 93.8%), suggestion acceptance (76.1% vs. 58.9%; p < 0.001), proactive disclosure (44.6% vs. 26.5%; p < 0.001), and fewer 7-day unplanned recontact (12.9% vs. 22.9%; p = 0.002). No unsafe recommendations or safety events were identified. CONCLUSIONS: Compared with text-based LLM, Dolphin improved emotional-semantic alignment and patient-education outcomes, supporting bimodal alignment as a strategy for reducing misalignment-driven communication failures. FUNDING: National Natural Science Foundation of China, State Key Laboratory Special Fund, and Chinese Academy of Medical Sciences Innovation Fund.

Humans↗

PubMed2026

Automated CEAP Classification of Venous Duplex Reports Using Multimodal Artificial Intelligence.

OBJECTIVE: To develop and internally validate a prototype multimodal artificial intelligence system for automated CEAP (Clinical, Etiological, Anatomical and Pathophysiological) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams. METHODS: Single centre retrospective observational study using routinely collected clinical data. One thousand consecutive venous duplex ultrasound reports from Cambridge University Hospitals NHS Foundation Trust, UK (July 2024 - May 2025) were labelled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (Great Saphenous Vein, Small Saphenous Vein, Deep system, Perforators), combined using late fusion with probability averaging. RESULTS: The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-AUC of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving SSV performance but reducing Deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70-0.92 and macro-AUC from 0.80-0.92. CONCLUSION: This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Despite class imbalance affecting minority class predictions, the strong discriminatory performance validates this multimodal ML model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision making.

Artificial intelligence↗

PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗

PubMed2026

Evaluation of a cornea-specialized large language model for diagnostic and management accuracy in complex corneal cases.

PURPOSE: To evaluate whether a cornea-specialized large language model (LLM) enhanced with retrieval-augmented generation (RAG) improves clinicians' diagnostic and management accuracy in complex corneal cases compared to a general-purpose GPT-4o model and unaided clinician performance. METHODS: This prospective, randomized, masked evaluation study involved three cornea trainees who each independently reviewed 39 real-world corneal cases under three experimental conditions: unaided, GPT-4o-assisted, and assisted by a cornea-specialized GPT-4o model. The cornea-specialized model was constructed by embedding over 200 publicly available Wikipedia articles into GPT-4o's RAG framework. Participants provided open-ended diagnoses and selected the next-step management options (multiple choice). They were allowed up to three GPT-4o queries per case, and the AI-assisted arms were randomized to minimize bias. Accuracy for both tasks was compared against expert reference standards using McNemar's test. RESULTS: Diagnostic accuracy was 48.7%, 20.5%, and 38.5% unaided, improving to 69.2%, 46.2%, and 59.0% with general GPT-4o (p<0.04). The cornea-specialized GPT-4o further improved accuracy to 71.8%, 48.7%, and 74.4%, with improvements over unaided performance for all clinicians (p<0.01). For next-step decisions, unaided accuracy was 76.9%, 87.2%, and 59.0%. With the specialized model, Ophthalmologist 3 improved to 71.8% (p<0.05), Ophthalmologist 1 remained high at 82.1%, and Ophthalmologist 2 declined to 64.1% (p<0.05). CONCLUSIONS: A cornea-specialized LLM enhanced with RAG improved diagnostic accuracy in complex corneal cases, particularly among clinicians with lower baseline performance. Effects on management accuracy were inconsistent. Future studies should explore the use of open-ended management tasks and examine whether smaller, curated retrieval corpora yield better model performance.

Humans↗

PubMed2026

BMT4me En Español: Multisite Feasibility and Usability Testing of a Spanish-Language mHealth Adherence Support App for Spanish-Speaking Caregivers of Children After Hematopoietic Stem Cell Transplantation and Cancer Treatment.

BACKGROUND: Medication nonadherence during the first 100 days after pediatric hematopoietic stem cell transplantation (HSCT) and during oncology treatment increases risk for complications. BMT4me is a caregiver-facing mobile health (mHealth) application providing medication reminders, symptom tracking, and note-taking features to support medication management. Spanish-speaking caregivers are frequently excluded from digital adherence interventions due to the lack of language-accessible tools. PROCEDURE: We conducted a multisite, mixed-methods usability testing of a Spanish-language version of BMT4me ("BMT4me en Español") with Spanish-speaking caregivers of children (ages 2-17 years) post-HSCT or with an oncology diagnosis on active treatment. Caregivers completed a facilitated, three-step usability session (unobtrusive observation, interactive observation, and debriefing), followed by a semi-structured interview, and then completed the system usability scale (SUS). Quantitative outcomes were summarized descriptively; qualitative data were analyzed using content analysis with constant comparison. RESULTS: Fifteen participants enrolled at each site for a total of 30 participants. Across both sites, the recruitment rate was 91%. All participants completed all parts of the study. The SUS score (M = 80.09; SD = 17.35) was above average (>68). Two key qualitative themes emerged: (1) the perceived positive impact of BMT4me on managing a serious illness and (2) the acceptance and sociocultural relevance of BMT4me for Spanish-speaking families. Caregivers also shared suggestions to add educational content and multiuser functionalities to BMT4me. CONCLUSIONS: The acceptance and perceived positive impact of the Spanish BMT4me app indicates that socioculturally relevant, Spanish mHealth interventions have strong potential to support Spanish-speaking caregivers in pediatric oncology and HSCT settings. CLINICAL TRIALS NCT: NCT06361173.

Adolescent↗

PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗

PubMed2026

Artificial Intelligence Cannot Replace Peer Reviewers but May Help Editors Triage: A Comparative Analysis of a Large Language Model and Human Reviewer Recommendations at the American Journal of Sports Medicine.

BACKGROUND: The peer review system faces increasing strain from rising manuscript volumes, reviewer fatigue, and well-documented interreviewer disagreement. Large language models (LLMs) have shown potential to support the peer review process, but their ability to replicate editorial decisions at high-impact medical journals and their utility as manuscript screening tools remain unknown. PURPOSE: To compare the agreement between an LLM and the final editorial decision on manuscripts submitted to the American Journal of Sports Medicine and to evaluate the potential of LLMs as a manuscript screening tool. STUDY DESIGN: Cross-sectional agreement study. METHODS: Fifty-four manuscripts randomly selected from submissions to the American Journal of Sports Medicine (September 2024-October 2024) were reviewed by a locally deployed LLM (Ministral 3 14B; Mistral AI) using a standardized prompt. The artificial intelligence (AI) produced a categorical recommendation (reject, cascade, revision, or accept) and a numerical score (0-100) for each manuscript. Agreement with the final editorial decision was assessed by Cohen kappa (4-category model) for pooled human reviewers (n = 139 reviews) and the AI (n = 54). Screening performance was evaluated by positive predictive value (PPV), sensitivity, and specificity. RESULTS: Pooled human reviewers demonstrated fair agreement with the final decision (κ = 0.181 [P < .001]; 42.4% agreement), while the AI demonstrated slight, nonsignificant agreement (κ = 0.126 [P = .099]; 37.0% agreement). The AI recommended revision for 61.1% of manuscripts, of which 72.7% were ultimately rejected or cascaded, demonstrating systematic "revision bias." When the AI recommended rejection, 54.5% of those manuscripts were ultimately rejected and 27.3% were cascaded; when the AI recommended cascade, 50% were rejected and 50% were cascaded. However, when the AI recommended rejection or cascade (n = 21), 90.5% received a final decision of rejection or cascade (PPV, 90.5%; specificity, 81.8%). Manuscripts with an AI score <70 were rejected or cascaded 88.0% of the time (PPV, 88.0%). CONCLUSION: AI cannot replicate the nuanced judgment of human peer reviewers at a high-impact sports medicine journal. When AI recommended rejection or cascade, 90.5% of manuscripts received that final decision (descriptive PPV, 90.5%; 95% CI, 71.1%-97.3%), suggesting potential utility as an exploratory first-pass screening tool warranting further validation in larger cohorts. However, AI could not reliably distinguish manuscripts destined for outright rejection from those that would be cascaded to a sister journal-an important limitation for editorial triage applications.

Sports Medicine↗

PubMed2026

Modulating sentence comprehension in people with aphasia through anodal tDCS: A double-blind randomized cross-over study.

This double-blind randomized cross-over study investigated the effects of perilesional anodal transcranial direct current stimulation (AtDCS) combined with speech-language therapy on sentence comprehension in eight individuals with chronic nonfluent agrammatic aphasia. The behavioral therapy consisted of an intensive comprehension treatment including drilling in sentence-to-picture matching and Mapping Therapy. Each participant underwent both the anodal tDCS and sham stimulation conditions (five received sham first followed by real stimulation, and the remaining three the reverse sequence), with each condition paired with the same behavioral treatment and separated by a four-month washout period. Stimulation was applied over the perilesional area (left BA6) for 20 min during daily 40-min therapy sessions over four consecutive weeks. Sentence comprehension was assessed with the RiComprendo battery and functional communication with the Communicative Effectiveness Index (CETI). Data were analyzed using paired t-tests, Bayesian analyses, and linear mixed-effects models to control for baseline performance and individual variability. Both stimulation conditions produced significant pre-to-post improvements in sentence comprehension, particularly for syntactically complex structures such as passives and center-embedded object relatives. However, gains were overall greater following AtDCS, as reflected in larger effect sizes, stronger Bayes factors, and a significant treatment effect in the mixed-effects models. Only the AtDCS condition yielded significant improvements in self-perceived comprehension abilities on the CETI. These findings suggest that AtDCS over perilesional cortical areas may boost the effects of traditional language therapy on sentence comprehension, supporting its feasibility and potential as an adjuvant intervention in post-stroke aphasia rehabilitation.

Humans↗

PubMed2026

Association between pulse pressure and markers of cognitive function: a systematic review and meta-analysis.

Our aim was to systematically review and meta-analyse evidence on the association between pulse pressure (PP) and cognitive function using PubMed, PsycInfo, Embase and Scopus (inception-July 2025) publication databases. Studies were included if they reported an association between PP and cognitive function and summarized narratively and by performing fixed-effects meta-analysis. The search identified 4171 publications with 43 studies meeting inclusion criteria. Domains assessed included global cognition, memory, language, attention, executive function, processing speed and visuospatial ability. Meta-analysis suggests a positive association between PP and global cognition, and a negative association with memory in both cross-sectional and longitudinal studies with inconsistent findings from narratively summarized studies. Processing speed, executive function and language negatively associated with PP in cross-sectional studies with limited evidence provided by longitudinal studies or narratively summarized studies. There was limited evidence of an association with attention and visuospatial ability.

Humans↗

PubMed2026

Your story, your brand: A career core competency for nurses.

Intentional management of one's professional story, or narrative discipline , is now a core competency and responsibility for nurses at all career stages. Workforce mobility, interdisciplinary collaboration, broadening career opportunities, and the expansion of digital platforms have elevated the importance of how nurses are perceived by colleagues, organizations, and the public. Increasingly, a nurse's professional story and digital footprint influence professional opportunities, career advancement, and even employment decisions.Many companies invest heavily in brand management to build trust and emotional connection with the people they serve. Importantly, narrative discipline also contributes directly to healthy work environments by reinforcing trust, role clarity, respect, and psychological safety. Drawing from leadership practice, emerging research on nurses' social media use, healthy work environment principles, and guidance from national nurse leadership organizations, this article outlines how nurses can align personal, professional, and enterprise identities; use language deliberately; and engage with discipline and integrity. Practical strategies are provided to help nurses move from passive narrative formation to intentional storytelling that supports career development, workforce engagement, organizational trust, and the sustainability of the nursing profession.

Humans↗

PubMed2026

The Role of Artificial Intelligence for Intimate Partner Violence Prevention: A Systematic Review.

INTRODUCTION: Intimate partner violence (IPV), encompassing physical, sexual, emotional and economic abuse, remains a pervasive global health concern. Traditional prevention efforts face obstacles such as underreporting, delayed detection and limited personalised support. Emerging artificial intelligence (AI) approaches offer new opportunities to enhance IPV prevention. AIM: This systematic review maps and synthesises evidence on AI-driven tools in IPV prevention based on studies published between 2004 and 2024. METHODS: Following PRISMA 2020 guidelines and PROSPERO registration, we searched PubMed, Embase, CINAHL, PsycINFO, IEEE Xplore and Web of Science. Eligible studies explicitly evaluated AI technologies targeting IPV prediction, screening, intervention or support delivery. Study quality was appraised using the Mixed Methods Appraisal Tool (MMAT). RESULTS: Of 1304 records initially identified, 41 studies met eligibility criteria. AI applications ranged from machine learning (ML) for risk prediction and natural language processing (NLP) for IPV detection in clinical and social media data, to image analysis for forensic evaluation and chatbot-based support. Predictive modelling demonstrated strong discriminative performance, while NLP-based screening detected IPV with notable sensitivity. Chatbots showed feasibility and user acceptability, but evidence of their direct impact on reducing IPV incidence was limited, with one randomised controlled trial showing a modest reduction. Key challenges identified included algorithmic bias, data privacy risks and barriers to integration across health and social care systems. DISCUSSION: AI-informed interventions show promise for improving IPV detection, risk assessment, and scalable support, but questions remain about long-term effectiveness, ethical fairness, transparency and equitable implementation. Future interdisciplinary research should address these concerns to responsibly deploy AI in IPV prevention. RELEVANCE TO CLINICAL PRACTICE: The findings highlight the importance of trauma-informed, culturally responsive care and provider training in AI applications. Nurse-led innovation and policy advocacy will be crucial for safe, equitable integration of AI in IPV prevention.

Artificial Intelligence↗

PubMed2026

Can ChatGPT Replace Human Clinical Coders? A Comparative Study in Otology Billing.

OBJECTIVE: Evaluate the utility of the large language model (LLM), ChatGPT, for the analysis of operative notes and the generation of Current Procedural Terminology (CPT) codes in comparison to human clinical coders. STUDY DESIGN: CPT billing codes assigned by ChatGPT were compared to existing billing data. Otology practice within a tertiary academic center. METHODS: About 191 operative notes from a single surgeon (9/2022-10/2023) were analyzed. ChatGPT-3.5 and 4 models were prompted for CPT codes based on operative notes. Assessment included determining exact and partial match rates, sensitivity and specificity for targeted procedures, and work Relative Value Units (wRVU) differences between ChatGPT-generated and human-assigned codes. RESULTS: ChatGPT-3.5 achieved exact matches in 22% of cases and partial matches in 32%, while ChatGPT-4 achieved 14% exact and 33% partial matches. When cochlear implantation (CI) was excluded, performance dropped significantly. For CI, ChatGPT-3.5 demonstrated a sensitivity of 94% and specificity of 90%, while ChatGPT-4 showed a sensitivity of 96% and specificity of 92%. In contrast, performance on cartilage grafting was poor, with sensitivities of 4.2% for ChatGPT-3.5 and 0% for ChatGPT-4. ChatGPT-3.5 and 4 showed moderate CPT code matching accuracy among themselves, with slight agreement to human coders. Both models tended to underbill for wRVUs compared to human coders, with significant differences in the values generated. CONCLUSION: This study assessed ChatGPT's effectiveness in automating CPT code assignment for otologic surgeries. While the models achieved high sensitivity values for assigning codes related to cochlear implantation, both models struggled with complex cases, failed to apply modifiers, and often assigned fewer wRVUs. The findings highlight ChatGPT's potential in medical billing but indicate a need for further refinement.

Humans↗

PubMed2026

Awake Craniotomy for Eloquent Region Glioblastoma Classified by Tumor Location-A Retrospective and Prospective Cohort Study.

INTRODUCTION: The efficacy of awake craniotomy (AC) with intraoperative mapping for glioblastoma (GBM) in eloquent regions remains debated. This study aims to evaluate functional and survival outcomes of GBM patients undergoing AC stratified by tumor locations. METHODS: A combined retrospective (2015-2023, n = 114: 43 AC vs. 71 standard craniotomy) and prospective cohort (2023-2025, n = 28: 13 AC vs. 15 standard craniotomy) of GBM patients with motor/language-eloquent tumors was analyzed. Tumors were classified into motor subtypes (I: precentral gyrus; II: premotor/supplementary motor; III: internal capsule posterior limb; IV: other) and language subtypes (I: Broca's/precentral; II: postcentral/supramarginal gyrus; III: Wernicke's; IV: insular; V: other). Outcomes included extent of resection (EOR), postoperative motor/language recovery, overall survival (OS), and progression-free survival (PFS). RESULTS: The retrospective cohort demonstrated that AC has advantages in functional preservation across various motor/language subtypes. However, AC was associated with significantly deteriorated survival outcomes specifically in precentral gyrus GBMs. A prospective cohort study, enrolling only precentral gyrus GBMs for validation, yielded results consistent with the retrospective findings: worsened OS and PFS (OS: HR = 3.223, p = 0.0450; PFS: HR = 2.374, p = 0.0476); reduced EOR (AC: 74.3% ± 5.3%; standard craniotomy: 86.9% ± 12.3%, p = 0.0470); and better motor recovery. CONCLUSIONS: Functional preservation and survival outcomes of AC in GBM exhibited subtype-specific correlations with tumor locations. AC with intraoperative mapping effectively preserves neurological function in GBM patients. However, for tumors involving the precentral gyrus, the AC approach carries greater risks than benefits and should be considered with caution. TRIAL REGISTRATION: Strategic Intervention on Preserving Motor Function During Awake Craniotomy: NCT05143788. Strategic Intervention on Preserving Language Function During Awake Craniotomy: NCT05143775.

Adult↗

PubMed2026

Comparing Traditional Motor Speech Practice to Contextualized Speech Practice in Preschoolers With Childhood Apraxia of Speech.

PURPOSE: The aim of this study was to compare retention of real-word targets across practice conditions (contextualized vs. motor-only) within a modified integral stimulation treatment for preschoolers with childhood apraxia of speech (CAS). METHOD: A single-subject experimental design with alternating treatments was used with matched target sets randomly assigned to contextualized practice, motor-only practice, or no treatment. Three preschoolers with CAS completed 18 therapy sessions, each consisting of two 25-min blocks: one contextualized practice and one motor-only practice. Order of practice was randomized each visit. Changes in percent phonemes correct (PPC) and lexical stress accuracy, derived from blinded transcription, were explored with visual analysis and effect sizes (standardized mean difference, d statistic). RESULTS: Meaningful improvements (d > 1) were observed in PPC across words treated in contextualized practice for all three children immediately posttreatment and for two of three children at the 1-month follow-up. Meaningful improvements in the motor-only condition were observed in two of three children immediately posttreatment and at follow-up. No meaningful changes were observed in lexical stress across any conditions in any participant. CONCLUSIONS: This study provides preliminary support for the feasibility of a modified integral stimulation therapy that incorporates elements of linguistically grounded therapies (linguistic retrieval, recasts, expansions) that may facilitate target retention in some preschoolers with CAS. However, other elements should be explored in conjunction with integral stimulation to maximize clinical outcomes. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.33228981.

Humans↗

PubMed2026

Functional Reading Activities to Motivate and Empower: Maintenance of Reading Outcomes for Young Adults With Intellectual and Developmental Disabilities Following a Randomized Controlled Trial.

PURPOSE: This study examined whether the effects of Functional Reading Activities to Motivate and Empower (FRAME), a functional, strategy-based reading comprehension intervention for young adults with intellectual and developmental disabilities (IDDs), were maintained 6 months following the completion of the intervention and explored participants' perceptions of the intervention's feasibility, relevance, and perceived impact. METHOD: Participants were 44 young adults with IDDs (ages 18-26 years) who participated in a previously reported randomized controlled trial (FRAME participants: n = 23; controls: n = 21). Trial outcomes were assessed via telepractice at pretest, posttest, and 6-month follow-up. Six-month maintenance analyses focused on outcomes that demonstrated significant posttest group differences: use of (a) reading comprehension strategies (proximal) and (b) reading comprehension questions (distal). Participant perceptions (social validity) were collected post-intervention from FRAME participants using a structured interview protocol with closed- and open-ended items. RESULTS: At the 6-month follow-up, FRAME participants demonstrated sustained but reduced improvements in strategy use relative to controls (p = .040). Between-groups differences were not maintained for reading comprehension questions (p = .091). Participants reported high acceptability and perceived relevance of FRAME, with qualitative themes reflecting perceived improvements in comprehension, self-improvement, and increased independence. CONCLUSION: Findings suggest that FRAME supports sustainable gains in reading comprehension strategy use and is perceived as meaningful and feasible for young adults with IDDs, although additional supports may be needed to promote sustained improvements in distal comprehension outcomes. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.33307218.

Humans↗