There is no such thing as a validated prediction model

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration

Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147

Internal validation of predictive models

Steyerberg, E. W., Harrell, F. E., Borsboom, G. J. J. M., Eijkemans, M. J. C., Vergouwe, Y., & Habbema, J. D. F. (2001) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(01)00341-9 · Cited by 2,746

Calibration: the Achilles heel of predictive analytics

On behalf of Topic Group ‘Evaluating diagnostic tests and prediction models’ of the STRATOS initiative, Van Calster, B., McLernon, D. J., van Smeden, M., Wynants, L., & Steyerberg, E. W. (2019) · BMC Medicine · doi:10.1186/s12916-019-1466-7 · Cited by 2,650

Derivation of a Simple Clinical Model to Categorize Patients Probability of Pulmonary Embolism: Increasing the Models Utility with the SimpliRED D-dimer

Anderson, D., Rodger, M., Ginsberg, J., Kearon, C., Gent, M., Turpie, A., Bormanis, J., Weitz, J., Chamberlain, M., Bowie, D., et al. (2000) · Thrombosis and Haemostasis · doi:10.1055/s-0037-1613830 · Cited by 1,651

Prognosis and prognostic research: validating a prognostic model

Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009) · BMJ · doi:10.1136/bmj.b605 · Cited by 1,436

Prediction models need appropriate internal, internal-external, and external validation

Steyerberg, E. W., & Harrell, F. E. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.04.005 · Cited by 1,304

Assessing the Generalizability of Prognostic Information

Justice, A. C., Covinsky, K. E., & Berlin, J. A. (1999) · Annals of Internal Medicine · doi:10.7326/0003-4819-130-6-199903160-00016 · Cited by 1,160

A calibration hierarchy for risk models was defined: from utopia to empirical data

Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina, M. J., & Steyerberg, E. W. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.12.005 · Cited by 910

Understanding receiver operating characteristic (ROC) curves

Fan, J., Upadhye, S., & Worster, A. (2006) · Canadian Journal of Emergency Medicine · doi:10.1017/s1481803500013336 · Cited by 843

Sample size considerations for the external validation of a multivariable prognostic model: A resampling study

Collins, G. S., Ogundimu, E. O., & Altman, D. G. (2016) · Statistics in Medicine · doi:10.1002/sim.6787 · Cited by 665

Additional file 1 of Predicting adverse outcomes in adults with a community-acquired lower respiratory tract infection: a protocol for the development and validation of two prediction models for (i) all-cause hospitalisation and mortality and (ii) cardiovascular outcomes

Rijk, M. H., Platteel, T. N., Geersing, G., Hollander, M., Dalmolen, B. L. G. P., Little, P. S., Rutten, F. H., van Smeden, M., & Venekamp, R. P. (2023) · Figshare · doi:10.6084/m9.figshare.24761522

Additional file 2 of Predicting adverse outcomes in adults with a community-acquired lower respiratory tract infection: a protocol for the development and validation of two prediction models for (i) all-cause hospitalisation and mortality and (ii) cardiovascular outcomes

Rijk, M. H., Platteel, T. N., Geersing, G., Hollander, M., Dalmolen, B. L. G. P., Little, P. S., Rutten, F. H., van Smeden, M., & Venekamp, R. P. (2024) · Figshare · doi:10.6084/m9.figshare.24761888

Additional file 1 of Pitfalls of single-study external validation illustrated with a model predicting functional outcome after aneurysmal subarachnoid hemorrhage

de Winkel, J., Maas, C. C. H. M., Roozenbeek, B., van Klaveren, D., & Lingsma, H. F. (2024) · Figshare · doi:10.6084/m9.figshare.26758733

Additional file 1 of External validation and update of the International Medical Prevention Registry on Venous Thromboembolism bleeding risk score for predicting bleeding in acutely ill hospitalized medical patients: a retrospective single-center cohort study in Japan

Arakaki, D., Iwata, M., & Terasawa, T. (2024) · Figshare · doi:10.6084/m9.figshare.25506577

Machine learning and statistical models for prediction of pregnancy outcomes in In vitro fertilization

McLernon, D. J., Nath, M., Shingshetty, L., & Bhattacharya, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26745-1.00011-6

External evaluation of the Manchester score in a contemporary SCLC cohort

Cunniffe, C., Sperrin, M., Walls, G., Blackhall, F. H., & Price, G. J. (2025) · Lung Cancer · doi:10.1016/j.lungcan.2025.108884

Comparative Validation of Five Early Trauma Scores for 24-Hour In-Hospital Mortality in Traumatic Brain InjuryPreprint

Chen, T., Tu, J., Liu, J., sun, H., Lu, Y., Chen, R., Hao, H., & Shen, C. (2026) · Research Square · doi:10.21203/rs.3.rs-8452508/v1

Development and evaluation of a 15-item health literacy classification model using integrated psychometric approaches in China

Tao, Z., Chen, L., Xu, Q., Zhu, Y., Ding, S., & Dong, Y. (2026) · BMC Public Health · doi:10.1186/s12889-026-26661-5

Artificial Intelligence in Electrocardiography and Echocardiography for Early Diagnosing Pulmonary Hypertension: A Systematic Review and Meta-Analysis (Preprint)Preprint

Liu, L., Nie, Z., Ganggayah, M. D., & Krishnasamy, S. (2026) · doi:10.2196/preprints.74956

An Improved Machine Learning Framework for Heart Disease Risk Prediction with Personalized Health Recommendations

Khantee, W., Somsuphaprungyos, S., & Natho, P. (2025) · International Conference on Robotics, Automation and Artificial Intelligence (RAAI) · doi:10.1109/raai67517.2025.11423419

Key factors of independent mobility of older adults: A data-driven approach based on a longitudinal survey in China

Jiang, Y., & He, S. Y. (2026) · Journal of Transport & Health · doi:10.1016/j.jth.2026.102293

Predictive performance precision analysis in medicine: identification of low-confidence predictions at patient and profile levels (MED3pa I)

Lefebvre, O., Lemyre, F. C., Éthier, J., Chikouche, L. H., Amriou, L., Poenaru, D., & Vallières, M. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag034 · Cited by 1

External validation of three risk prediction models for deep vein thrombosis in patients with acute stroke: a single-center cohort study

Fu, L., Feng, K., Cui, C., Li, L., Zou, B. J., & Liu, N. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1753784

Comparison of prostate cancer diagnostic models based on PCA3 and TMPRSS2:ERG RNA biomarkers from post-DRE and non-DRE urine specimens

Lopeta, M., Palikša, S., Belevičius, J., Ukanis, M., Kasiliauskienė, L., Dziuge, L., Žutautaitė, I., Barisiene, M., Bakavičius, A., Mickevičius, R., et al. (2026) · World Journal of Urology · doi:10.1007/s00345-026-06396-z

A machine learning–assisted approach to explain key determinants for achieving live birth in autologous ICSI cycles

Fraire-Zamora, J. J., Correa, N., Lattes, K., Popovic, M., & Rodríguez-Aranda, A. (2026) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-026-03869-3

External validation of PREDICT Breast v3.1 for overall survival in international cohorts, including young and invasive lobular subgroups

Verheul, E. M., Doornkamp, F., Petrov, I., Siesling, S., Lingsma, H. F., Koppert, L. B., Vreven, L. W. A., Voogd, A. C., Karsten, M. M., Doppelbauer, L., et al. (2026) · Breast Cancer Research and Treatment · doi:10.1007/s10549-026-07958-w · Cited by 1

Traditional statistics and artificial intelligence-based prognostic models for predicting type 2 diabetes mellitus after gestational diabetes: a systematic review

Yimer, N. B., Belsti, Y., Seifu, B. L., Goldstein, R., Teede, H. J., & Enticott, J. (2026) · Diagnostic and Prognostic Research · doi:10.1186/s41512-026-00229-8

Clinical AI/ML Model Testing and Validation

Pandey, A., Gupta, P., & Sehgal, N. K. (2026) · Springer eBooks · doi:10.1007/978-3-032-10808-1_6

Artificial intelligence-enabled electrocardiography for detection of left ventricular systolic dysfunction: A systematic review and meta-analysis

Munir, L. Z., Cai, A., Cai, A., Debs, C., Jesudason, D., Leslie, A., Mutahar, D., Zaka, A., Kovoor, J. G., & Gupta, A. (2026) · International Journal of Cardiology Innovations · doi:10.1016/j.ijci.2026.100005

Schatzkästchen oder Büchse der Pandora? – Klinische Ethik und Künstliche Intelligenz

Beil, M., Janssens, U., & Fuest, K. (2026) · Springer eBooks · doi:10.1007/978-3-662-72378-4_16

Diagnostic predictive models for cancer-related fatigue: current evidence and future directions

Liang, F., Wang, M., Li, K., Lv, D., Qian, T., & Bu, Z. (2026) · Supportive Care in Cancer · doi:10.1007/s00520-026-10822-8

Cross-region generalizability of early childhood development prediction models across South Asia: Evidence from harmonized MICS surveys

Hossain, M. M., Jahan, I., Ahmed, A. B., Sanu, M. A., & Rois, R. (2026) · Clinical Epidemiology and Global Health · doi:10.1016/j.cegh.2026.102407

A system for independent evaluation of clinical prediction models while preserving intellectual property and data privacyPreprint

Ensor, J., CALSTER, B. V., Barreñada, L., Wynants, L., & Perry, B. I. (2026) · Research Square · doi:10.21203/rs.3.rs-10767123/v1 · Cited by 1

AI-ECG performance for left ventricular systolic dysfunction: Reading beyond the pooled estimate

Goings, D. (2026) · International Journal of Cardiology Innovations · doi:10.1016/j.ijci.2026.100008

CORR Insights®: Development and Internal Evaluation of EAST-BMS (East Asian Survival Tool for Bone Metastasis Surgery): A Multinational Machine-learning Survival Prediction Model for Patients Undergoing Surgery for Nonspinal Bone Metastasis in East Asia

Groot, O. Q. (2026) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000004024

Real-World Generalizability of Alzheimer’s Volumetric MRI Machine-Learning Models: External Validation with British Data

Pereira, H. R., Diogo, V. S., Fonseca, J., Ferreira, H. A., & Prata, D. P. (2026) · Clinical Neuroradiology · doi:10.1007/s00062-026-01688-8

Integrating chemokine signatures and multi-omic biomarkers to predict immunotherapy response in non-small cell lung cancer: a comprehensive narrative review

Cabezón‐Gutiérrez, L., Palka-Kotlowska, M., Custodio‐Cabello, S., Rosero-Rodriguez, A. C., & Chacón-Ovejero, B. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1830731

Undertraining is the floor, not the ceiling: when statistical and validation failures compound in radiomics

Koçak, B., & Meşe, İ. (2026) · European Radiology · doi:10.1007/s00330-026-12730-1

Validation of the UB‐ROSC Score for Predicting OHCA Survival in Chiayi City, Taiwan

Chang, W. T., Chou, C.-C., & Tsai, C.-C. (2026) · Emergency Medicine International · doi:10.1155/emmi/5871234

An Automated Listener-Effort Measure for ALS Generalizes across Diverse Platforms and ProtocolsPreprint

Roitberg, E. G., Trevisan, M. A., Peller, J., Sevlever, F., Aguirre, F., Shalom, D. E., Taitz, A., Bujía, G., Schwartz, J., Osik, J., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9849001/v1

Artificial intelligence and circulating tumor DNA minimal residual disease in perioperative resectable non-small cell lung cancer

Chen, Y., Li, W., Qiu, P., Zhong, R., Jiang, B., He, M., Chen, P., Liu, H., Gao, R., Li, X., et al. (2026) · Artificial Intelligence Surgery · doi:10.20517/ais.2026.27

Development and temporal validation of an interpretable machine learning model with a prototype web-enabled decision-support framework for early identification of depression risk in middle-aged and older adults using the CHARLS cohortPreprint

Yang, Y., Wen, M., Huang, T., Liu, X., & Zeng, X. (2026) · Research Square · doi:10.21203/rs.3.rs-10261069/v1

Prognostic Clinical Predictive Models for Dental Caries Using Artificial Intelligence: Methodological Considerations

Uribe, S. E., Carrasco‐Labra, A., Schwendicke, F., & Maldupa, I. (2026) · Caries Research · doi:10.1159/000553309

Development and Validation of an Early Risk Prediction Model for Sepsis-Induced Coagulopathy Based on Machine Learning

Yang, Q., Liu, F., Ren, C., Xiong, W., Zou, J., Yang, L., & Zeng, Q. (2026) · Clinical and Applied Thrombosis/Hemostasis · doi:10.1177/10760296261468959

Haemodynamic intolerance at the de-resuscitation decision in critically ill adults: development, temporal and external validation of a prediction model across two intensive care databasesPreprint

Shao, L., Li, Y., Xu, C., Tai, H., Wang, L. Q., & Shi, J. (2026) · Research Square · doi:10.21203/rs.3.rs-10050782/v1

Advancing cardiovascular risk stratification in COPD: methodological gains and remaining gaps

Samarasinghe, Y., & Sadatsafavi, M. (2026) · European Respiratory Journal · doi:10.1183/13993003.00497-2026

Does cross-wave validation overestimate screening performance? An ID-isolated evaluation of model complexity for possible sarcopenia in CHARLSPreprint

Yue, Y., & Zhang, C. (2026) · Research Square · doi:10.21203/rs.3.rs-10135331/v1

External validation of a head & neck cancer outcome prediction model in radiotherapy patients: Evaluating generalisability in a geographically diverse population, with particular consideration of older patients

Aly, F., Descallar, J., Robledo, K. P., Sundaresan, P., Miller, A., Vinod, S. K., & Holloway, L. C. (2026) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2026.111714

Prediction Models for Lower Limb Lymphedema after Surgery in Patients with Gynecological Malignancies: A Systematic Review

Li, H., Zhang, Y., Li, W., & Lu, Y. (2026) · Lymphatic Research and Biology · doi:10.1177/15578585261470704

Development and external validation of a text-matching algorithm to identify patients with lower extremity deep venous thrombosis from radiology reports at five medical centers

Birrenkott, D. A., Stubblefield, W. B., Altheimer, A., Ouadah, S. J., Griffey, R. T., Asaro, P. V., Beckman, J. A., Fermann, G. J., Keller, J. L., Storrow, A. B., et al. (2026) · Thrombosis Research · doi:10.1016/j.thromres.2026.109796

Determinanten für den Rehabilitationserfolg und die Inanspruchnahme von Gesundheitsleistungen bei Personen mit starken chronischen unspezifischen Kreuzschmerzen

Soff, J. (2025) · Elsevier eBooks · doi:10.1016/b978-3-437-45065-5.00034-x

Duplicate Clinical Profiles as a Source of Data Leakage in Heart Disease Classification: A Benchmark Audit of Validation, Calibration, and Explainability

KİLİM, O. (2026) · Advances in Artificial Intelligence Research · doi:10.54569/aair.1998842

Prediction models for adverse events from prostate cancer curative radiotherapy: a systematic review of methodological quality

Oliveira, C. C., Jongen, C. A. M., Mikolić, A., Incrocci, L., Lingsma, H. F., Roobol, M. J., Korfage, I. J., Heemsbergen, W. D., van Klaveren, D., Romero, C., et al. (2026) · Translational Oncology · doi:10.1016/j.tranon.2026.102961

A machine learning approach to predict treatment response in myofascial pain patients receiving masseter trigger point injections

Shabaan, A. A., Kassem, I., Mahrous, A. I., Aboulmagd, I., Amer, I. A., Shaaban, A., Fathy, K. K., Ragab, R., El-Ghafour, M. A., Ibrahim, S., et al. (2026) · BMC Oral Health · doi:10.1186/s12903-026-09491-0

From Explanations to Reflective Questions in Human-AI Decision-Making

Fischer, S. W. S., Holmberg, L., Thill, S., & Schraffenberger, H. (2026) · Mensch und Computer (MuC) · doi:10.1145/3820253.3831391

Weighing Ischemic Stroke and Bleeding Risks: Evaluating Discrimination and Calibration in Patients With CKD and Atrial Fibrillation

de Jong, Y., Langenhuijsen, L. F. S., & van Diepen, M. (2026) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2026.06.004

From prediction to decision: prediction-based decision rules and target trial emulation in ADHD

Garcia‐Argibay, M., Faraone, S. V., Chang, Z., Cortese, S., & Larsson, H. (2026) · The Lancet Psychiatry · doi:10.1016/s2215-0366(26)00227-0

Distinguishing Organizing Pneumonia from Malignancy During Cancer Surveillance: Predominant Diagnostic Value of Imaging over Systemic Inflammatory Biomarkers

Yeşilçay, H. B., & Aydın, A. A. (2026) · Current Oncology · doi:10.3390/curroncol33080491

Risk prediction rules in individuals at risk of rheumatoid arthritis: systematic review and validation using the APIPPRA cohort

Jasenecova, M., Harker, G., Adas, M. A., Qureshi, S., Duquenne, L. M., Wilson, M., Hensor, E. M. A., Emery, P., Mankia, K. S., Bechman, K., et al. (2026) · EULAR Rheumatology Open · doi:10.1016/j.ero.2026.100228

Development and Cross-Database Evaluation of 6-Hour Landmark Prediction Models for a Subsequent Packed Red Blood Cell Transfusion Record in Adult Trauma ICU PatientsPreprint

Zhang, G., Yang, C., Jiang, S., Li, X., & Duan, H. (2026) · Research Square · doi:10.21203/rs.3.rs-10708009/v1

Cytokine-augmented risk stratification for six-month incident nephritis in children with IgA vasculitis

Liu, F., Pan, Y., Ding, Y., & Wang, D. (2026) · Frontiers in Pediatrics · doi:10.3389/fped.2026.1902940

Risk-Stratified CQI in Engineering Education: Six-Cohort Validation of a Registry-Only Early-Warning Rule

Rahim, A. A. A., Abdullah, N. E., Shauri, R. L. A., & Janin, Z. (2026) · IEEE International Conference on Innovation, Ethics & Emerging Tech in Engineering and Computing Education (IE2C) · doi:10.1109/ie2c69620.2026.11663032

Development and external validation of a prediction model for acute kidney injury after coronary artery bypass graftingPreprint

Nie, J., Xu, T., Wang, R., Zhou, W., Tong, Y., Chen, J., Zhang, X., & Song, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-10593383/v1

External validation of prediction scores of positive random skin biopsy in suspected intravascular large B-cell lymphoma

Segawa, Y., Okamoto, A., Sumiyoshi, S., Watase, H., Iwata, M., Tomita, A., & Terasawa, T. (2026) · Journal of the Formosan Medical Association · doi:10.1016/j.jfma.2026.08.053

Development and external validation of a machine learning screening model for broad sleep health disturbance in patients with osteoarthritisPreprint

Chen, Q., Chen, J., Xie, C., Chen, Y., Zheng, S., Shen, G., & Yang, W. (2026) · Research Square · doi:10.21203/rs.3.rs-10528907/v1

Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration

Yapıcı, F. (2026) · Bioengineering · doi:10.3390/bioengineering13091036

Consensus framework for the validation of generative AI: call for collaborators on the Validation Accords

Huo, B., Chartash, D., Tsang, L., Collins, P. G. S., Cacciamani, G. E., Ruurda, J. P. H., Mayol, J. M., Hinchliffe, R. J., & Guyatt, G. (2026) · Nature Medicine · doi:10.1038/s41591-026-04647-5

Predicting postoperative acute kidney injury after colorectal cancer surgery: development and internal validation of a clinical prediction model using a nationwide cohort

Larsen, M. S., Munch, P. V., Heide‐Jørgensen, U., Iversen, L. H., & Christiansen, C. F. (2026) · BJS Open · doi:10.1093/bjsopen/zrag124

Temporal and cross-site validation of an AI system for self-harm detection

Rozova, V. S., Chen, L., Witt, K., Conway, D. M., Robinson, J., & Verspoor, K. M. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001667

A composite tool for predicting gestational diabetes or hypertensive disorders of pregnancy: development and temporal validation

Moran, L., Dhungana, R. R., Dawadi, S., Rolnik, D. L., Wang, R., Mousa, A., Tay, C. T., Palmer, K. R., Mol, B. W., Teede, H. J., et al. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111932

Clinical, CT Radiomic, and Pathomic Models for FOS Immunohistochemical Status Defined by the Training-Cohort Median in Ovarian Cancer

Qin, L.-H., Lü, Y.-X., Liang, W., Song, R., Chen, P.-Y., Xu, W., Zeng, G., Chen, J., Zhao, Y., & Long, L. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.08.110

Evaluating national machine-learning predictions of household biogas use across time and local contexts in NepalPreprint

Cao, R., & An, L. (2026) · Research Square · doi:10.21203/rs.3.rs-10843496/v1

Spatial Transferability of Explainable Machine Learning for Predicting Pre-Treatment Tuberculosis Loss to Follow-up Across West Java DistrictsPreprint

Ilyas, R., Putri, D. I. P., Apriani, L., & istiqammah, I. (2026) · Research Square · doi:10.21203/rs.3.rs-10736091/v1

Trustworthy AI for disease pathogenesis and precision medicine: A disease-agnostic framework from data to deployment

Chi, H., Liu, X., Yan, Y., Wang, L., Luo, P., Su, L., Jiang, L., Zhou, X., Huang, G., Jiang, Y., et al. (2026) · Genes & Diseases · doi:10.1016/j.gendis.2026.102480 · Cited by 1

Machine learning–enabled vibrational spectroscopy for translational bioclinical applications

Egbewole, B. I., Ilesanmi, T. M., Olatunde, S. K., Bamidele, D. B., Ogbonna, C. E., & Olawade, D. B. (2026) · Microchemical Journal · doi:10.1016/j.microc.2026.119784

Hiding in plain sight: finding undiagnosed chronic kidney disease without kidney function data through a machine learning modelPreprint

Amorocho-Morales, J. D., Peláez, S., Forero, J. F., Liscano, J., & Correa‐Morales, J. E. (2026) · Research Square · doi:10.21203/rs.3.rs-10543145/v1

An independent generative verification agent for in vitro fertilization outcome prediction: Conditional score-based diffusion modeling to cross-check black-box classifiers

Sergeev, S., Diakova, I., & Nadirashvili, L. (2026) · Journal of IVF-Worldwide · doi:10.46989/001c.171311

Development of risk prediction model for delirium using routinely collected electronic health records

Gokhale, S., Belsti, Y., Taylor, D., Zeps, N., Nagpurkar, M., Teede, H., & Enticott, J. (2026) · Archives of Gerontology and Geriatrics Plus · doi:10.1016/j.aggp.2026.100338

Reproducibility in biomedical research

Zhong, J., D’Angelo, D., Lyu, C., Xu, I., Enns, P. K., Wang, F., & Kaushal, R. (2026) · Nature Medicine · doi:10.1038/s41591-026-04667-1

It Knows My Emotions Better Than I Do: Digital Phenotyping and the Contest Over Symptom Interpretation

Zhang, X., Li, H., & Bao, S. (2026) · Integrative Psychological and Behavioral Science · doi:10.1007/s12124-026-10032-1

Askisi-MD: Development and Validation of a Web-Based Neuropsychological Screener for Mathematical Learning Difficulties Using Explainable Machine Learning

Zygouris, N. C., Styliaras, S. K., Vlachos, F. M., Evaggelos Spyrou, A., Patrikelis, P., Messinis, L., & Nasios, G. (2026) · Journal of Integrative Neuroscience · doi:10.31083/jin52375

Beyond CEA and CA19-9: Is the TDCP index ready for clinical use in rectal cancer?

Ando, M., Matsumura, T., & Fukami, Y. (2026) · Surgical Oncology · doi:10.1016/j.suronc.2026.102583

Development of a Prognostic Model and Risk Factor Analysis for Spontaneous Intracerebral Hemorrhage Patients Based on Clinical and Laboratory IndicatorsPreprint

He, X., Wang, Y., Chen, Q., Yang, H., Geng, Z., Liu, J., Yin, X., Rong, Y., zhang, S., WANG, G., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10702588/v1

Belgian validation of the INFLUENCE 3.0 model predicting 5-year locoregional and contralateral breast cancer recurrence

der Vorst, A. V., Grimberg, C., van Maaren, M. C., Hueting, T. A., Weltens, C. G., Siesling, S., & Groothuis‐Oudshoorn, C. G. M. (2026) · The Breast · doi:10.1016/j.breast.2026.104944

Incremental Diagnostic Value of Cell-Block-Based Targeted BRAF and RAS Testing in TBSRTC Category III–IV Thyroid Nodules: A Single-Center Retrospective Study

Topal, C. S., Duymaz, Y. K., Sönmez, B., & Ülgür, H. Ş. (2026) · Diagnostics · doi:10.3390/diagnostics16193258

Methodological quality and clinical applicability of obesity-related disease risk prediction models: a systematic review and meta-analysisPreprint

Wang, L., Wang, M., Chen, Z., Chuanlin, C., Ma, Y., Sui, M., & 应晓华 (2026) · Research Square · doi:10.21203/rs.3.rs-10892305/v1

How much reported heart-disease machine-learning performance survives honest validation? A duplicate-, hospital- and calibration-aware audit of public dataPreprint

Sultan, M. A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7624476

‘From exacerbation counts to calibrated risk: lessons from ACCEPT 3.0-UK for COPD prediction models’

Antón, A. J., Romero, P., & Navarrete, B. A. (2026) · Thorax · doi:10.1136/thorax-2026-225547

Explainable Classification Models for Identifying Elevated Gastrointestinal Symptom Burden in Honduran Communities: A Cross-Sectional Study

Flores, E., Meza, D., Gonzales, R., Agudelo-Santos, C., Sosa, L., Espinoza, L. C., & Zablah, I. (2026) · Diagnostics · doi:10.3390/diagnostics16213440

A Peritonitis Risk Rate Prediction System for Pediatric Peritoneal Dialysis with eXplainable AIPreprint

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