{"id":"W4407419631","doi":"10.1007/s11936-025-01077-3","title":"Harnessing iPSCs to Model Marfan Syndrome: Advancing Clinical Diagnosis and Drug Discovery","year":2025,"lang":"en","type":"article","venue":"Current Treatment Options in Cardiovascular Medicine","topic":"Connective tissue disorders research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"London Health Sciences Centre; Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research; Heart and Stroke Foundation of Canada","keywords":"Medicine; Drug discovery; Marfan syndrome; Intensive care medicine; Internal medicine; Bioinformatics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001861878,0.000540875,0.0006760493,0.0008067595,0.0002181333,0.001697032,0.0007173147,0.001312215,0.002055319],"category_scores_gemma":[0.00159266,0.0002245504,0.0005431765,0.0003207985,0.0007476912,0.0009465132,0.0007723251,0.003277783,0.0007293167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003546133,"about_ca_system_score_gemma":0.0006327297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00033519,"about_ca_topic_score_gemma":0.0005682769,"domain_scores_codex":[0.9993597,0.0001834279,0.00006329179,0.000076029,0.0002598923,0.00005766841],"domain_scores_gemma":[0.9991417,0.0004147737,0.0001159566,0.0001302669,0.0001184598,0.00007869503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006590522,0.000619359,0.003919511,0.001782435,0.0002471542,0.001810494,0.0004424165,0.006887158,0.7210284,0.04602758,0.01391975,0.2026566],"study_design_scores_gemma":[0.0005000946,0.003799691,0.006030121,0.001299619,0.000888398,0.007037505,0.0006460087,0.02907215,0.5623844,0.0430874,0.3451063,0.0001482323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4226747,0.1653341,0.3132129,0.02661451,0.006856333,0.001172434,0.006066088,0.002538427,0.05553056],"genre_scores_gemma":[0.8234555,0.095287,0.06223046,0.005197288,0.001309357,0.0007280942,0.003346508,0.0002129317,0.008232834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002055319,"threshold_uncertainty_score":0.009846687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04507601335016404,"score_gpt":0.3948967761009626,"score_spread":0.3498207627507986,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}