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PubMed2026

The Effect of Game-Based Virtual Reality Rehabilitation and Its Impact on Upper Extremity Function After Arthroscopic Rotator Cuff Repair: A Randomized Controlled Trial.

BACKGROUND: Arthroscopic rotator cuff repair (ARCR) often results in prolonged recovery and limited shoulder function. Conventional physical therapy rehabilitation programs require sustained patient engagement; however, adherence is frequently low. Game-based virtual reality (VR) offers an interactive and engaging environment that may enhance rehabilitation outcomes. OBJECTIVE: To evaluate the effect of a game-based VR program on the function of the upper limb in patients following ARCR. METHODS: A randomized controlled trial was conducted with patients who underwent ARCR. Participants were randomized into two groups: game-based VR or conventional rehabilitation. Outcomes were evaluated using the Disabilities of the Arm, Shoulder and Hand score, pain severity by the Numerical Pain Rating Scale, range of motion measures, and muscle strength testing. Assessments were performed at baseline and at 6 weeks and 12 weeks post surgery. RESULTS: Results have shown significant within-group improvements in pain, function, range of motion, and isometric muscle strength across all time points (P < 0.05). Between-group analysis revealed greater improvements in pain, function, flexion range, and abduction and external rotation strength in the experimental group at both time points (P < 0.05). Abduction range improved significantly only at 12 weeks (P = 0.02), whereas external rotation range showed no significant difference between groups at either time point (P > 0.05). CONCLUSION: The findings indicate that integrating game-based VR rehabilitation provides additional benefits over conventional therapy in improving pain and upper extremity function following ARCR. These findings support the use of VR as an effective alternative to the conventional rehabilitation for postoperative rehabilitation.

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PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

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PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

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PubMed2026

Could the preoperative urethral curve be used to predict immediate urinary continence following Retzius-sparing robot-assisted radical prostatectomy? A retrospective multi-center study.

PURPOSE: Immediate urinary continence (UC) recovery following Retzius-sparing robot-assisted radical prostatectomy (RS-RARP) remains highly variable, highlighting the need for reliable preoperative prediction. We aimed to develop and validate models to identify patients likely to achieve immediate UC recovery following RS-RARP. MATERIALS AND METHODS: A total of 580 prostate cancer patients who underwent RS-RARP from four medical centers were assigned to a training set (n=348), an internal validation set (n=103) and an external validation set (n=129). Independent predictors were identified through univariate analysis and LASSO regression. A nomogram was constructed using multivariate logistic regression. Its performance was evaluated with receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis. RESULTS: Immediate UC recovery was observed in 84.5% (294/348) of patients in the training cohort, 80.6% (83/103) in the internal validation cohort, and 81.4% (105/129) in the external validation cohort, respectively. Multivariate analysis identified membranous urethral length (MUL) (OR=1.23, P=0.029) and urethral curvature (OR=2.84, P<0.001) as independent predictors, while prostate volume (PV) (OR=0.84, P <0.001) as a protective factor. The nomogram integrating MUL, PV, and urethral curvature demonstrated superior predictive accuracy, with an AUC of 0.87 (95% CI, 0.83-0.91) in the training cohort. The bootstrap-corrected calibration slope was 0.96, and the Brier score was 0.08. Calibration curves and decision curve analysis confirmed the predictive accuracy and clinical utility of the nomogram. CONCLUSIONS: Our study introduces a novel quantitative method for assessing urethral curvature. The mpMRI-based model, integrating urethral curvature and prostate spatial configuration, offers enhanced predictive accuracy for postoperative immediate UC recovery.

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PubMed2026

Loneliness and Personality: Noise- and Bias-Free True Correlations Between Loneliness and the Big Five Personality Domains.

OBJECTIVE: While loneliness is intertwined with many mental and physical health problems, its origins are not yet well understood. We sought to better understand its link to personality in a large national cohort. METHODS: Combining self- and informant ratings in multiple samples, we conducted the largest study to date to examine loneliness' true correlations (rtrues) with the Big Five personality traits, free of single-method biases and transient and random errors. RESULTS: Across three samples (Estonian-speaking, N = 20,893; Russian-speaking, N = 762; English-speaking, N = 599), we found a strong relationship between loneliness and Neuroticism (rtrue = 0.60-0.70). Loneliness also had robust but much weaker associations with Extraversion (rtrue = -0.20 to -0.30), and only weak associations (rtrue = 0.10 to -0.20) with Agreeableness, Conscientiousness, and Openness. Collectively, the Big Five accounted for over 50% of loneliness variance. In a subsample, the associations were only slightly smaller longitudinally over approximately 10 years. CONCLUSION: Overall, feeling lonely is more closely related to Neuroticism than previously understood, and the association endures over time.

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PubMed2026

Risk Factors and Predictive Model for Postoperative High Myopia in Children Undergoing Congenital Cataract Surgery With Intraocular Lens Implantation.

PURPOSE: To identify risk factors associated with the development of high myopia following congenital cataract surgery and to establish a robust predictive model. DESIGN: Retrospective clinical cohort study. SUBJECTS: This retrospective study included 106 pediatric patients who underwent congenital cataract surgery with primary IOL implantation (mean follow-up 8.19 years). The model was externally validated in an independent cohort of 72 patients with a mean follow-up of 7.83 years. METHODS: Preoperative and postoperative ocular biometric parameters were collected. Risk factors for postoperative high myopia were analyzed using Cox proportional hazards regression, which served as the basis for model construction. The predictive performance of the model was rigorously evaluated for discrimination and calibration. Discriminative ability was quantified using Harrell's C-index and the area under the receiver operating characteristic curve (AUC). Model calibration was assessed via calibration plots by comparing predicted probabilities with actual observed outcomes. Internal validation was performed using a bootstrapping method (500 iterations) to ensure model stability and adjust for potential overfitting. RESULTS: An initial postoperative refraction of <+0.75D, and a higher IOL Power to Axial length Ratio (IOL/AL ratio) were identified as significant risk factors for the development of postoperative high myopia. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. The predictive model demonstrated robust performance, achieving a C-index of 0.711 (internal validation C-index: 0.713). The area under the receiver operating characteristic curve (AUC) values for predicting high myopia at 5 and 10 years were 0.858 and 0.745, respectively. Furthermore, calibration curves demonstrated excellent agreement between the predicted and observed outcomes throughout the follow-up period. In external validation, the model achieved a C-index of 0.825, 5-year AUC of 0.833, and 10-year AUC of 0.713. CONCLUSIONS: Our analysis established that initial postoperative refraction <+0.75D, and an elevated IOL/AL ratio are key determinants of high myopia risk following surgery. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. This predictive framework provides clinicians with a practical tool to optimize preoperative IOL selection and identify high-risk infants who require vigilant myopia prevention and balanced amblyopia management.

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PubMed2026

Transverse Tibial Transport for Limb Salvage in Ischemic Lower Extremity Disease: Technique, Mechanisms, and Clinical Outcomes-A Systematic Review.

Transverse tibial transport (TTT) is a surgical technique derived from Ilizarov's distraction osteogenesis principles that stimulates angiogenesis and microcirculatory regeneration in the ischemic lower limb without directly manipulating macrovascular anatomy. By creating a proximal tibial cortical bone window and distracting it transversely using an external fixator, TTT triggers converging cascades of growth factor release, endothelial progenitor cell mobilization, and immunomodulation that translate into improved distal limb perfusion and wound healing. Combined TTT plus endovascular therapy improves amputation-free survival versus endovascular therapy alone. Prospective randomized trials and standardized international protocols are needed to consolidate TTT's role in multidisciplinary limb salvage pathways.

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PubMed2026

Predictive Models for Hypoglycemia Risk in Haemodialysis Patients With Diabetic Kidney Disease: Systematic Review and Meta-Analysis.

AIM: To provide evidence for selecting and developing reliable clinical assessment tools for hypoglycemia in diabetic kidney disease patients during haemodialysis. DESIGN: Review. METHODS: Systematic searches were performed in 9 Chinese and English databases to collect literature regarding the development of hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease. Two reviewers independently performed literature screening, data extraction, risk-of-bias assessment, and applicability evaluation. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability of the included studies. Meta-analysis was conducted using R software. DATA SOURCES: CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, EMbase, Web of Science, and CINAHL. The search period covered from the establishment date of each database to December 2025. RESULTS: Six studies, comprising six prediction models, were included. Two studies performed internal validation, and three conducted external validation. All models reported the area under the curve, ranging from 0.813 to 0.866, and calibration measures. Four studies were rated as having a high risk of bias, while all six demonstrated good overall applicability. The meta-analysis showed that the pooled AUC value of the six studies was 0.846 (95% CI: 0.823-0.867). CONCLUSION: Research on hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease remains in the developmental stage. Although the included prediction models exhibited satisfactory apparent discriminatory ability and clinical applicability, most of the original studies suffered from a high risk of bias and lacked adequate validation. The true predictive performance and clinical application value of these models remain to be further verified. Accordingly, routine and unconditional clinical application is not recommended at this stage. Future studies should include more high-quality, multicenter external validation and develop models with high generalizability, favourable clinical applicability, and robust predictive performance to facilitate early identification of hypoglycemia risk in this population. IMPACT: This study systematically evaluated the hypoglycemia risk prediction models for diabetic kidney disease patients during haemodialysis, and the research on hypoglycemia risk prediction models for maintenance haemodialysis patients during dialysis is still in the development stage. This study provides a reference for clinical medical staff to select or develop hypoglycemia risk prediction and assessment tools for diabetic kidney disease patients during haemodialysis. REPORTING METHOD: This study was conducted in accordance with the relevant guidelines of the EQUATOR Network and followed the TRIPOD-SRMA Checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: PROSPERO: CRD420251243352.

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PubMed2026

Sarcomas: Research on Ultrarare Subtypes Gains Ground.

Rare cancers account for almost one fourth of all cancers. Sarcomas belong to the group of cancerous diseases with an incidence of less than 6 cases per 100,000 inhabitants. Over the past decade, activities were launched worldwide to elucidate the peculiarities of many of the more than 100 sarcoma subtypes described in the World Health Organization handbook. The major contributor to exact diagnosis is molecular pathology. The subgroup of ultrarare sarcomas (URS) poses a significant problem as each URS type has its own morphology, biology, natural history, and prognosis. In 2020, 35 international sarcoma centers agreed to standards of evaluating URS. The threshold was set to an incidence of less than 1 case per 1,000,000 inhabitants, and 77 URS subtypes were defined. Also quality criteria for centers to be selected for retrieving data to registries were consented. This issue of Cancer Epidemiology, Biomarkers & Prevention contains the first article to validate these principles of URS using the data from a nationwide cancer database. The authors from Taiwan also pointed out limitations of the approach. Combination with the national death database allowed to calculate overall survival (OS) and identified age as a significant factor for OS per URS type. These new data might foster future research on diagnosis and treatment of URS. See related article by Lee et al., p. 1654.

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PubMed2026

The role of simulator immersion on learning and transfer of decision-making skill in sport.

Virtual reality has become popular in sport and other domains because it can immerse the user within a sporting context and solve logistical problems for additional off-field training. There is limited evidence, however, of whether immersion is crucial for learning and transfer. This study compared training of decision-making skill between 360-degree video virtual reality (360VR) and two-dimensional video. Twenty-eight Australian Rules Football players were randomly assigned to one of three training groups: 360VR, two-dimensional video, and control. Across four weeks, participants in the training groups were exposed to decision-making scenarios consisting of visual, contextual and auditory cues. Performance was assessed pre- and post-training with virtual reality and field-based decision-making tests. Results indicated that the two-dimensional video training group showed significantly superior decision-making in the field-based transfer test compared to 360VR and control groups post intervention. There was also indication that two-dimensional video training was superior to the control post intervention in the virtual reality test. Findings indicate that immersion created in virtual reality is not an underpinning mechanism for learning and transfer, rather the use of perceptual information is crucial. 360VR may facilitate uptake through engagement, but two-dimensional video is adequate for learning and transfer of decision-making to the field.

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PubMed2026

A systematic review of human avoidance learning: Cognition, computation, and methods.

Avoidance behaviour is fundamental for survival but can become maladaptive in clinical conditions. A large body of literature has accumulated on the dynamics of human avoidance learning. However, current theories and overviews do not provide an exhaustive account of this evidence. In this systematic review, we identify N = 116 studies on human avoidance learning. We analyse these studies with the goal of distilling robust empirical phenomena as a basis for theory-building, and examine their diagnostic value in differentiating between competing theories. We find that the evidence is difficult to reconcile with foundational two-factor and classical safety-signal accounts, and most strongly supports expectancy- and inference-based views, in which avoidance responses are selected with respect to represented consequences. At the same time, no current framework provides a complete account of the evidence: several findings point to an additional role for operant valuation, Pavlovian influences, and contextual or latent-state control over the expression of avoidance. Methodologically, we observe that the problem setting in the most common experimental paradigms is radically simpler than real-world avoidance and therefore unlikely to expose the limits of inferential or reflective mechanisms. Consequently, we argue that paradigms with greater computational demands and more realistic action affordances are required to identify the mechanisms underlying avoidance learning. Collectively, these insights provide a foundation for theoretical refinement, computational modelling, and methodological innovation, with implications for advancing interventions targeting maladaptive avoidance.

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PubMed2025

Mediators of Change in Cognitive Behavioral and Mindfulness-Based Online-Interventions for Hypoactive Sexual Desire Dysfunction in Women.

Low sexual desire is a common sexual problem among women. When it is accompanied by significant personal distress, it may be diagnosed as hypoactive sexual desire dysfunction (HSDD). Both cognitive behavioral therapy (CBT) and mindfulness-based therapy (MBT) are effective treatments for HSDD when delivered in person or online. In this randomized controlled treatment study, CBT and MBT consisted of eight guided self-help modules delivered online, and participants completed measures at pretreatment and after 3, 6, and 12 months. Nine variables were examined as potential mediators of treatment outcomes (i.e., sexual desire and sexual distress), namely mindfulness, self-compassion, rumination, body connection, self-consciousness, relationship satisfaction, sexual communication, depression, and anxiety. In total, 212 women diagnosed with HSDD were randomized to either CBT or MBT (Mage = 36.3, SD = 10.2). Improvements in self-compassion, rumination, body connection, and self-consciousness partially mediated treatment outcomes in at least one of the treatment groups. Mediation effects were mostly small, explaining up to 15% of the total effects. No systematic differences in mediation pathways between CBT and MBT were found. These findings emphasize the importance of emotion regulation, metacognitive processes, and embodiment for the effective treatment of HSDD. Future research should refine treatment components to enhance efficacy and ensure that psychological interventions adequately address common concerns among women with HSDD.

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PubMed2026

A Meta-analysis of the intervention effects of ball sports on children with autism Spectrum disorder.

PURPOSE: To evaluate the overall efficacy of ball sports interventions for children with autism spectrum disorder (ASD) and to explore potential moderators that may influence intervention outcomes. METHODS: Randomized controlled trials were retrieved from nine databases (e.g., CNKI, PubMed) from 2000 to April 2025. Meta-analysis was conducted using RevMan 5.4 and Stata 18.0, with subgroup analyses by age, duration, frequency, session length, and ball sport type. RESULTS: A total of 14 randomized controlled trials involving 406 children were ultimately included. Meta-analysis results indicated that ball sports had a significant overall intervention effect on children with ASD (SMD = -1.12, 95% CI: -1.56, -0.68, P < 0.05), with significant improvements observed in social skills (SMD = -0.55), verbal communication (SMD = -0.85), and behavioral problems (SMD = -1.10). Subgroup analysis indicated that interventions involving mini-basketball or other ball sports with simple rules-conducted over a 12-week period, 3-5 times per week, for 30-60 min per session-yielded better outcomes, with children aged 3-6 years showing more consistent benefits. CONCLUSION: Ball-based interventions effectively alleviate core ASD symptoms. Structured moderate-intensity programs are recommended as adjunct therapies. Future studies should standardize protocols and examine long-term benefits.

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PubMed2026

The impact of an intact rotator cuff on the outcomes of reverse shoulder arthroplasty: a meta-analysis of 20,924 patients.

BACKGROUND: While reverse shoulder arthroplasty (rTSA) is commonly utilized for rotator cuff tear arthropathy, indications have expanded to include, primary glenohumeral osteoarthritis (GHOA) with intact cuff. The presence of an intact cuff may influence outcomes after rTSA because preserved cuff musculature can contribute to shoulder stability and force which could potentially improve postoperative function and reduce complication rates. However, studies have reported contradictory results on whether or not an intact cuff would provide better outcomes in patients receiving an rTSA. METHODS: This is a systematic review and Meta-analysis performed according the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Cochrane, Embase, and Google Scholar (pages 1-20) were queried through December 2025. Inclusion criteria consisted of studies comparing the outcomes of rTSA based on whether patients had a diagnosis of GHOA with intact cuff, or had a deficient rotator cuff (ie had a diagnosis of rotator cuff tears without OA, or cuff tear arthropathy). Extracted data included adverse events, improvement in patient reported outcome measures, and improvement in range of motion. RESULTS: Eleven retrospective articles and 1 prospective article met the inclusion criteria with 4,542 in the GHOA with intact cuff group and 16,382 in the cuff-deficient group (cuff tear arthropathy: 15,423 patients; rotator cuff tear: 959 patients). Patients undergoing rTSA for GHOA and intact cuff had a lower rate of revisions (odds ratio [OR] = 0.53; 95% CI: 0.41- 0.68, P < .001; I2 = 0%), overall complications (OR = 0.57; 95% CI: 0.46-0.71, P < .001; I2 = 0%), acromial stress fracture (OR = 0.22; 95% CI: 0.08- 0.59, P = .003; I2 = 0%), infection (OR = 0.43; 95% CI: 0.26- 0.73, P = .002; I2 = 37%), and instability (OR = 0.60; 95% CI: 0.40- 0.90, P = .01; I2 = 0%). In addition, GHOA patients had a better improvement in both American Shoulder and Elbow Surgeons scores (mean difference = 7.17; 95% CI: 2.13- 12.21, P = .005; I2 = 81%) without exceeding the minimal clinically important difference, and external rotation (mean difference = 12.00°; 95% CI: 9.63- 14.37, P < .001; I2 = 38%). CONCLUSION: Rotator cuff-deficient patients undergoing rTSA have a higher risk of postoperative complications compared to patients undergoing rTSA for GHOA with an intact cuff. They also showed less improvement in American Shoulder and Elbow Surgeons scores and external rotation. However, the clinical significance of these differences should be interpreted with caution, as not all improvements exceeded established thresholds for clinical importance.

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PubMed2026

Mendelian randomisation for rheumatology: beyond hype-what it's good for, what it can't do, and how to read it critically.

Mendelian randomisation (MR) has become abundant in the literature, with variation in quality and frequent overinterpretation of causality. This creates a problem for clinical readers, reviewers, and editors: some MR studies can sharpen causal thinking, prioritise drug targets, and challenge misleading observational claims, whereas others are little more than automated exposure-outcome scans with causal claims disproportionate to the evidence. MR can strengthen causal inference when randomised trials are impractical and conventional observational studies are vulnerable to confounding, reverse causation, or selection bias. In rheumatology, credible MR can contribute to questions about disease aetiology, modifiable risk factors, therapeutic target validation, adverse-effect anticipation, and phenotype validation. However, its interpretation depends on whether the exposure is plausibly instrumentable, whether the genetic instruments are biologically defensible, whether assumptions are interrogated in ways appropriate to the design, and whether findings are triangulated with clinical, observational, experimental, and mechanistic evidence. Instead of recapitulating all methodological issues of MR, this review aims to help rheumatologists distinguish robust MR from weak or overinterpreted analyses quickly. We provide an accessible framework for reading and triaging MR studies in rheumatology. Papers that use poorly justified instruments, treat medication use as drug-target evidence, interpret genetic liability as diagnosis, rely on mechanical sensitivity analyses, ignore prior evidence or ask no clinically meaningful question can often be passed over by readers. The goal is not to discourage MR in rheumatology, but to raise the standard; useful MR should clarify causal reasoning rather than simply generate another statistically significant association.

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PubMed2026

Hierarchical modeling of tumor subtypes in cell lines using large-scale genomic datasets.

Cancer cell lines (CLs) are widely used to study tumor biology and drug response, yet their translational relevance is often limited by inaccurate subtype annotations. Existing CL-tumor matching approaches are frequently constrained by flat classification schemes, weak subtype definitions, and the exclusion of normal tissue references, leading to potential confounding of tumor-specific and tissue-of-origin signals. To address these limitations, a hierarchical classification (HC) framework is presented in which CLs are aligned with patient tumors across biological resolutions, from organ to molecular subtype. Gene expression profiles from 802 CLs, 5,612 tumors from The Cancer Genome Atlas (TCGA) , and 8,939 non-cancerous tissues were integrated to separate oncogenic signals from tissue-specific signals. Node-specific features were selected using maximum relevance minimum redundancy, and balanced accuracies of 89% in cross-validation and 75%, and 80% on external datasets were achieved. Through the framework, 43 CLs were reassigned, and clinically relevant underrepresented subtypes were identified.

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PubMed2026

Driven toward care, avoiding the end: A systematic review and meta-analysis of the relationship between death anxiety and healthcare utilisation.

Both overuse and underuse of the healthcare system have been recognised as significant problems. Relatedly, growing research has recognised the key role of death anxiety in driving various health-relevant behaviours. However, the relationship between death anxiety and healthcare utilisation has not yet been systematically explored. The current systematic review and meta-analysis addressed this gap. In total, 987 papers were screened for inclusion, of which 63 were included in the final review (Ntotal = 21,271). This included 33 quantitative studies, 27 qualitative studies and 3 mixed-methods designs. In total, 17 studies contained sufficient data to be meta-analysed. Overall, the included studies highlighted a significant relationship between death anxiety and healthcare utilisation; in particular, positive associations with desire for life-prolonging treatments and contact with hospitals and medical professionals. By contrast, a negative association was found with other aspects of healthcare utilisation, including hospice use and end-of-life communication. The sample type emerged as a significant moderator, suggesting that the relationship between death anxiety and healthcare usage was strongest in non-medical samples. The current findings suggest that death anxiety plays a key role in utilisation of the healthcare system. The fear of death may need to be targeted in psychological interventions, in order to ensure maximal effectiveness of health services, and improve outcomes for healthcare users.

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PubMed2026

Are There Any Effective Behavior Change Strategies for Communicating Genetic Risk in Obesity Prevention and Body Weight Reduction Interventions?

This systematic review examined how differences in intervention components may contribute to inconsistent findings in genetic risk communication studies, addressing obesity-related outcomes (e.g., weight reduction, nutrition behavior, exercise). The review was preregistered (PROSPERO #CRD42024524026) and followed PRISMA guidelines. Searches across eight databases identified 23 randomized controlled trials, covering 18 intervention trials. Risk of bias was assessed using the Risk of Bias 2 tool. A narrative synthesis was used to cluster studies by the content of intervention and control groups. Genetic risk communication alone (no behavioral counseling, addressing nutrition and exercise) or combined with phenotype-based risk was ineffective and sometimes counterproductive among low-risk individuals. When combined with personalized behavioral counseling, effectiveness improved, but only when compared to waitlist control groups or non-personalized behavioral counseling. Significant effects emerged in high-genetic risk subgroups within personalized behavioral counseling, using behavior change techniques such as problem-solving, feedback on behavior, self-monitoring, and environmental changes. The most promising results emerged from complex interventions integrating genetic risk communication into multiple sessions and combining numerous additional behavioral change techniques, such as social reward, cues/prompts, self-reward. Complex personalized interventions combining multiple behavior change techniques and prompting experiential genetic risk awareness show promise for improving weight, nutrition, and exercise-related outcomes.

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