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PubMed2026

Examining the impact on quality-of-life of those living with inherited optic neuropathies: an updated systematic review.

BACKGROUND: Inherited optic neuropathies (IONs) cause progressive visual loss and substantially affect quality of life. This updated systematic review aims to synthesize and evaluate evidence on the patient experience of living with IONs, identify currently used assessment tools, and highlight areas for improvement in research and clinical care. METHODS: We systematically searched MEDLINE, EMBASE, PsycINFO, CINAHL, and Scopus for studies on (i) inherited optic neuropathies; (ii) quality of life or health status; (iii) patient-reported outcome measures; and/or (iv) qualitative research. Inclusion was restricted to peer-reviewed studies. Screening, data extraction, and risk of bias assessment were conducted using Covidence. RESULTS: From 1775 unique records, 5 studies met the inclusion criteria. They evaluated quality-of-life outcomes in patients with Leber hereditary optic neuropathy or autosomal-dominant optic neuropathy across 6 countries. Patient-reported outcome measures used included National Eye Institute Visual Functioning Questionnaire-39, National Eye Institute Visual Functioning Questionnaire-25, Short-Form 12 Health Survey, Beck Depression Inventory, and Pittsburgh Sleep Quality Index. Only one study used qualitative interviews to explore the emotional, social, and financial impacts on patients and families. DISCUSSION: This updated systematic review builds upon previous work to further characterize the impact of ION on patients living with these conditions and the tools currently used to assess them. We highlight the need for a multifaceted approach to ION management, combining medical treatment with comprehensive psychosocial support to work toward more patient-centred care and improved quality of life for those living with these challenging conditions.

Humans↗
PubMed2026

Symptom assessment and health-related quality of life in children with lower urinary tract dysfunction: A retrospective cross-sectional study.

INTRODUCTION: Lower urinary tract dysfunction (LUTD) significantly impacts children's health-related quality of life (HRQoL), but key determinants of this impairment remain incompletely understood within a patient-centered framework. METHODS: This retrospective cross-sectional study enrolled 272 children with LUTD. Clinical symptoms were assessed using the Dysfunctional Voiding Scoring System (DVSS) and the Overactive Bladder Symptom Score (OABSS), and HRQoL via the Pediatric Quality of Life Inventory (PedsQL&#x2122;) 4.0 Generic Core Scales. RESULTS: The mean total PedsQL score was 83.66 &#xb1; 16.07, with the School Functioning domain being the most impaired (73.75 &#xb1; 20.94). Univariate analysis identified older age (&#x2265;11 years), daytime urinary leakage, overactive bladder (OAB) symptoms, and abnormal DVSS scores as factors associated with reduced HRQoL (all P < 0.05); multivariate regression further confirmed only older age and abnormal DVSS were independent influencing factors (both P < 0.001). A significant negative correlation existed between the total DVSS score and the total PedsQL score (r = -0.390, P < 0.01), indicating that greater symptom severity predicts poorer overall HRQoL. CONCLUSION: This study indicates that in children with LUTD, the severity of voiding dysfunction (quantified by DVSS) is inversely correlated with HRQoL. Voiding dysfunction impairs multiple domains of daily functioning, including physical well-being, emotional adjustment, social interaction, and school performance. Shifting from a symptom-focused to a patient-centered care model and integrating routine HRQoL assessment into standard clinical management can optimize clinical practice for pediatric LUTD.

Humans↗
PubMed2026

Journey Mapping of the Patient Experience from Diagnosis to End of Life in Lung Cancer: A Qualitative Meta-Synthesis.

OBJECTIVES: This study aimed to systematically synthesize the lived experiences and journey narratives of lung cancer patients across disease stages, and identify key tasks and pain points during the disease course through patient journey mapping, providing evidence for comprehensive disease management throughout the patient journey. METHODS: Ten databases, including PubMed, Embase, Web of Science, Scopus, PsycINFO, CINAHL, Cochrane Library, CNKI, Wanfang, and SinoMed, were systematically searched, with a search period from database inception to August 15, 2025. The JBI Critical Appraisal Tool for qualitative studies was used to evaluate the quality of studies, and the results were integrated using a meta-aggregative approach. RESULTS: Thirteen studies were included. Based on the patient journey mapping, the lung cancer patient journey comprises four potential stages: evaluation and diagnosis, initial treatment, maintenance therapy, and end-of-life. A total of 30 themes emerged within three dimensions: tasks, emotions, and pain points. Each dimension of each stage consists of 2-3 themes. CONCLUSION: The journey of lung cancer patients is protracted and complex, characterized by stage-specific needs and challenges. Future management strategies should be tailored to these distinct phases, providing precision supportive care to optimize treatment outcomes and enhance patients' quality of life. IMPLICATIONS FOR NURSING PRACTICE: This Patient Journey Map integrates routine clinical pathways with patients' lived experiences across each stage, revealing stage-specific challenges and providing targets for tailored nursing interventions. The framework promotes multidisciplinary, digitally enabled supportive care and indicates the importance of including patients' social circles to enhance patient-centered outcomes.

Humans↗
PubMed2026

A systematic review and meta-analysis of the late positive potential and internalizing psychopathology.

The present study leveraged the Hierarchical Taxonomy of Psychopathology (HiTOP) framework to conduct a systematic meta-analysis to determine the association between the late positive potential (LPP) index of emotional reactivity and internalizing psychopathology. PRISMA guidelines were followed. Articles were identified through PubMed, APA PsycInfo, and Web of Science online platforms in May 2025. Included articles examined associations between the LPP to positive and/or negative stimuli and internalizing psychopathology. Risk of bias and publication bias were assessed. Results were examined for individual disorders, distress and fear subfactors, and the internalizing spectrum using two approaches: standard analyses that examined aggregate effects and hierarchical analyses that examined direct and indirect relationships. We conducted moderator analyses for sample, task design, LPP quantification, and psychopathology measurement. We included 63 studies across 5,360 participants (Mage = 19.65, SD = 11.1; 58.7% female). In standard meta-analyses, depression was associated with a smaller LPP to positive stimuli (r = -.06, 95% confidence interval [CI; -.12, -.003]). Specific phobia was associated with a larger LPP to negative stimuli (r = .21, 95% CI [.02, .37]). Distress was associated with a smaller LPP to both positive (r = -.12) and negative (r = -.11) stimuli when measured via clinical interview, and fear was associated with a larger LPP to negative stimuli (r = .10, 95% CI [.03, .16]). Hierarchical analyses indicated that the depression results were specific to the disorder, whereas the fear disorder-level results were due to the higher order fear subfactor. The LPP demonstrates discriminant relationships with distress and fear disorders and subfactors. Results were largely robust against methodological factors. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Humans↗
PubMed2026

Interventions to improve school attendance: a systematic review and meta-analysis of evidence from randomised controlled trials.

BACKGROUND: Poor school attendance adversely affects youth behaviour and health trajectories. We reviewed and synthesised the evidence from randomised controlled trials (RCTs) on interventions to improve school attendance. METHODS: This systematic review and meta-analysis updates the Education Endowment Foundation (EEF) 2022 review on attendance interventions. We synthesised RCT data to inform recommendations. We searched ERIC (via EBSCOhost), PsycINFO, Web of Science and Google Scholar for publications 1 January 2020-16 October 2024 to identify attendance interventions delivered to students, parents/guardians or school staff. We also extracted and analysed RCTs identified in the EEF review. Two reviewers independently extracted data from published articles and assessed risk of bias and certainty of evidence (Grading of Recommendations Assessment, Development and Evaluation, GRADE assessment). We synthesised and meta-analysed data per our protocol (PROSPERO: CRD42024610037). RESULTS: We screened 9366 titles and abstracts and included 61 articles (2000-2024): 43 articles published 2020-2024 plus 18 articles from the previous review (2000-2020), reporting 57 trials of 54 interventions. Pooled mean difference and 95% CIs for days of school attended was 0.63 (-0.34 to 1.61), I2=64.0%, low certainty, for mentoring interventions and 0.43 (0.10 to 0.76), I2=91.9%, moderate certainty, for parental engagement interventions. Other intervention areas including targeted approaches, behavioural programmes and social-emotional interventions had smaller, heterogeneous evidence bases. Risk of bias was moderate-to-low in most trials. CONCLUSION: A large range of interventions exist to address the diverse causes of school absence. Parental engagement demonstrated a small positive effect on attendance, particularly among younger age groups. Most trials were from the USA; implementation and evaluation in other countries will inform effectiveness.

CHILD↗
PubMed2026

Factors influencing the enhancement&#xa0;of the new iron triangle&#xa0;in healthcare organisations.

PURPOSE: A new paradigm, "healthcare's new iron triangle," has been developed to emphasise the technological perspective of healthcare delivery, focusing on automation, value and empathy. The study aims to build a conceptual model and to identify factors for the enhancement of the new iron triangle in healthcare organisations. DESIGN/METHODOLOGY/APPROACH: The healthcare organisation is the primary focus point of the current study. To determine the factors, a survey of the literature and healthcare experts' opinions was conducted. The&#xa0;healthcare professionals validated the identified factors. Data for this study were gathered using a closed-ended questionnaire and scheduled interviews. The study employed "Total Interpretive Structural Modeling methodology and Matriced' Impacts Croise&#xb4;s Multiplication Appliqu&#xe9;&#xb4; a UN Classement/Cross-Impact Matrix Multiplication Applied to a Classification (MICMAC) analysis" to address the "why" and "how" the factors interact and prioritise the identified factors. FINDINGS: The study found that organisational structure (F8), artificial intelligence (F1), innovation (F2) and human resources (F5) are the driving or key factors of the study. RESEARCH LIMITATIONS/IMPLICATIONS: The study primarily focused on identifying factors for the enhancement of a new iron triangle in healthcare organisations. The scope could eventually be expanded to explore more areas. PRACTICAL IMPLICATIONS: Academics and other stakeholders will have a better understanding of the key drivers for the enhancement of the new iron triangle in healthcare organisations. ORIGINALITY/VALUE: In this study, total interpretive structural modeling and cross-impact MICMAC analysis are proposed as an innovative approach to address the new iron triangle in healthcare organisations.

Humans↗
PubMed2026

Meaning in Life in Palliative Cancer Care: Psychosocial and Existential Outcomes-A Systematic Review.

BackgroundExistential distress, marked by hopelessness, loss of meaning, and spiritual suffering, is prevalent among patients with advanced illness, and is associated with psychological burden and a wish to hasten death (WTHD).PurposeThis systematic review aimed to synthesize current evidence on meaning in life (MIL) in adult palliative care (PC) populations, focusing on its associations with quality of life (QOL), mental health, existential and spiritual well-being (SWB), and WTHD.MethodsMEDLINE, Web of Science, Scopus, and the Cochrane Library were searched for eligible studies (English, 2016-2024) involving adult cancer patients receiving PC. MIL was examined as a central intervention component or outcome. Risk of bias was assessed: findings were synthesized narratively. The review was registered in PROSPERO.ResultsEight studies (n&#x2009;=&#x2009;1733 participants) were included: four cross-sectional, two randomized controlled trials, one longitudinal observational study, and one qualitative study. Several studies had small samples and substantial attrition. Risk of bias was high (n&#x2009;=&#x2009;7), and moderate in one cross-sectional study. MIL was inversely associated with depression, anxiety, demoralization, and WTHD; and positively associated with QOL and SWB. MIL may also mediate psychological outcomes (eg, purpose, coherence, and personal values). However, heterogeneity in MIL conceptualization and measurement, combined with low methodological quality, limited comparability and certainty of findings.ConclusionMIL may be relevant to psychosocial/existential outcomes in PC. Conclusions are constrained by a small and methodologically weak evidence base. Further high-quality, longitudinal research is needed before MIL-centered interventions can be recommended for routine clinical practice.

Humans↗
PubMed2026

Long-term outcomes of surgical correction of ventral penile curvature in children: Patient-reported measures, surgical results, and decisional regret.

INTRODUCTION: There is a dearth of data on long-term outcomes, including patient-reported outcomes (PROMs), decisional regret, and complication rates for surgical correction of isolated ventral penile curvature in childhood. PATIENTS AND METHODS: Twenty-six children treated for ventral curvature between 1993 and 2008 were identified; 24 met inclusion criteria (isolated ventral curvature without hypospadias or need for urethral reconstruction). Surgical correction consisted of degloving alone or degloving with Nesbit-like dorsal plication when residual curvature >20&#xb0; persisted after degloving. PROMs were collected via pre-mailed validated questionnaires after puberty: Danish Prostatic Symptom Score (DAN-PSS) for LUTS, Erection Hardness Score (EHS) for erectile function, Penile Perception Score (PPS) for cosmetic perception, and items assessing decisional regret and perceived appropriateness of surgical timing. RESULTS: Curvature was corrected intraoperatively in all 24 patients. Twelve underwent degloving alone and 12 required additional dorsal plication. During long-term follow-up (median 14.2 years), one patient (4%) underwent re-operation for residual curvature, and three (13%) underwent cosmetic revisions; two (8%) underwent cystoscopy for flow concerns. 71% returned PROMs at a median age of 16.2 years. LUTS were uncommon, with low bother scores. Erectile function was favorable: 87% (13/15) reported EHS 4 and 93% (14/15) reported ejaculation. Cosmetic outcomes were favorable, with PPS dissatisfaction rates comparable to controls. Two patients reported dissatisfaction with overall appearance, and one with residual subjective curvature. No patient expressed decisional regret, and 88% felt timing of surgery was appropriate. CONCLUSION: Early surgical correction of isolated ventral penile curvature using degloving with or without dorsal plication provided durable anatomical correction and favorable long-term functional and cosmetic outcomes. These findings support early intervention as an effective approach with sustained patient-perceived benefits.

Humans↗
PubMed2026

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗
PubMed2026

Implicit and explicit statistical learning in reading: Evidence from a randomized controlled-learning study and computational modeling.

A key challenge in reading acquisition is understanding how learners extract the complex probabilistic mappings between print, meaning, and sound. Statistical learning (SL) theory offers a mechanistic account of how such mappings are acquired, whether implicitly through exposure or explicitly through instruction. We conducted a randomized controlled-learning study in Chinese, a writing system characterized by multiple sub-lexical regularities linking orthography, semantics, and phonology. Ninety-five 2nd-3rd graders with or at risk for dyslexia were randomly assigned to one of three groups: an implicit-SL training group exposed to repeated lexical and sublexical orthography-semantics-phonology associations, an explicit-SL training group receiving the same input plus explicit instruction on the sublexical print-sound mapping, and a no-SL control group. Both SL groups outperformed controls on the characters they were trained on, as well as on untrained characters that required generalization. However, only the explicit group demonstrated abstraction of print-sound mapping to novel items. Neural network simulations further revealed distinct mechanisms supporting implicit and explicit SL, consistent with a dual-system account of reading acquisition. Together, these findings (1) clarify how implicit and explicit learning distinctly support the discovery of statistical structure in written language and (2) underscore the implicit-explicit dual learning mechanism underlying reading acquisition.

Humans↗
PubMed2026

Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS.

BACKGROUND: Accurate classification of missense variants remains a challenging task despite major advances in genomics. Numerous computational models have been developed to assist in variant classification, but often require repeated integration and benchmarking efforts. Ensemble methods have been proposed to overcome the limitations of single predictors, but mostly rely on fixed, predefined weights that constrain their ability to capture interactions among predictive signals. METHODS: We present GenixRL, a dynamic ensemble framework that reformulates model fusion as a reinforcement learning optimization problem. GenixRL uses a Q-learning agent to learn a policy that dynamically weights the probabilistic outputs of complementary predictors, including BayesDel (addAF and noAF), ClinPred, and MetaRNN. Replacing static weighting with policy learning allows GenixRL to adaptively identify optimal weightings and substantially improve classification accuracy. RESULTS: In benchmark evaluation against 25 state-of-the-art predictors, GenixRL achieved an AUROC of 0.9644 on an independent ClinVar dataset. On saturation genome editing assays for BRCA1 and BRCA2, GenixRL achieved the best performance and ranked highest on 14 of 17 clinically significant genes in a zero-shot evaluation. Applied to uncertain and conflicting ClinVar variants, GenixRL enabled tiered, evidence-based prioritization of hundreds of thousands of variants as likely pathogenic or pathogenic with high confidence, supported by orthogonal population evidence from gnomAD. CONCLUSION: GenixRL advances pathogenicity prediction for missense variants and provides an adaptive ensemble that sorts variants of uncertain significance into tiered candidates for expert curation and functional validation.

Mutation, Missense↗
PubMed2026

A Meta-learning-driven strategy for adulteration detection in sweet potato starch and vermicelli using Raman spectroscopy.

To address the widespread adulteration of sweet potato starch and its vermicelli with cheaper starches and overcome conventional supervised learning's dependency on large labeled datasets, this study developed a few-shot discrimination method integrating Raman spectroscopy with meta-learning. We constructed a meta-learning framework using cassava- and wheat-adulterated sweet potato starch as the source domain for training, with potato-adulterated sweet potato starch and cassava-adulterated sweet potato vermicelli as two target domains for testing. Raman spectra showed high consistency between sweet potato vermicelli and its raw starch, laying the foundation for cross-domain detection. Testing yielded comprehensive classification accuracies of 95.33% and 98.00% for the two target domains, significantly outperforming SVM, RF, and CNN (max. 85.24%). This approach effectively identifies subtle starch variety differences in complex adulteration, providing novel food quality inspection solutions and verifying the feasibility of raw material-to-finished product cross-domain detection.

Ipomoea batatas↗
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.

Humans↗
PubMed2026

Artificial intelligence in treatment prediction for skeletal Class III malocclusion: A systematic review.

In skeletal Class III patients, treatment options range from orthodontics to orthognathic surgery. Choosing the optimal approach requires a comprehensive clinical evaluation, which may be supported by AI tools. The aim of this study was to assess the performance of AI models in predicting the need for orthognathic surgery and in identifying predictors influencing treatment decisions. A PRISMA-guided electronic database search (PubMed, Web of Science; 2009-2024; English/French) was performed to identify studies using machine learning (ML) or deep learning (DL) on cephalometric and clinical data. After screening and assessment for eligibility, 15 studies were critically appraised. Model performance was summarized using accuracy, sensitivity, specificity, and the area under the curve (AUC). ML algorithms (particularly Random Forest and XGBoost) and DL models (ResNet-based convolutional neural networks (CNNs)) achieved high accuracy for predicting surgical need. Frequently selected predictors included Wits appraisal, ANB angle, the maxillomandibular ratio (Mx/Md), overjet, and the divergence of the lower gonial angle. AI methods show promise for assisting treatment decisions in Class III malocclusion, with Random Forest and XGBoost performing well on tabular cephalometric data and CNNs on imaging. Larger, multicentre datasets and external validation are needed to improve reliability, address bias, and support clinical implementation.

Humans↗
PubMed2026

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

Humans↗
PubMed2026

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89-0.94) in cross-validation and 0.88 (95% CI: 0.85-0.91) in independent validation, outperforming clinical models (&#x394;AUC = +0.18, p < 0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression-free survival (HR = 4.7, p < 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC.

Humans↗
PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗