{"id":"W4404754793","doi":"10.1182/bloodadvances.2024014929","title":"Generalized additive logit models for clinical prediction","year":2024,"lang":"en","type":"article","venue":"Blood Advances","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Government of Canada; Statistics Canada","funders":"","keywords":"Logit; Logistic regression; Generalized additive model; Econometrics; Mixed logit; Statistics; Mathematics; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.009338573,0.001790697,0.004046669,0.002499748,0.0008537064,0.00347547,0.004491722,0.003175044,0.01158902],"category_scores_gemma":[0.0312894,0.001467028,0.00233713,0.003433063,0.001526203,0.003913415,0.002258159,0.004942068,0.003540055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002167755,"about_ca_system_score_gemma":0.002049234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01797673,"about_ca_topic_score_gemma":0.01788034,"domain_scores_codex":[0.9935217,0.004632837,0.0002418326,0.0007139806,0.0004557681,0.0004339114],"domain_scores_gemma":[0.9714891,0.02485109,0.0009631803,0.001216538,0.001116675,0.0003634774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006133508,0.0004067171,0.008395348,0.0004814309,0.0007642298,0.0004284145,0.0003153582,0.6366388,0.0002600496,0.1956529,0.02403358,0.1320098],"study_design_scores_gemma":[0.0000358777,0.00003697237,0.0008107324,0.00004449298,0.00007846416,0.00005375341,0.00003707904,0.8553183,0.00005822926,0.1411551,0.002337256,0.00003370751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03513112,0.007387602,0.939615,0.006302908,0.0006366009,0.0001524639,0.004016788,0.002280544,0.00447702],"genre_scores_gemma":[0.7987968,0.007297848,0.1393936,0.00150621,0.001634413,0.00077349,0.005325441,0.0005410042,0.0447312],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01797673,"threshold_uncertainty_score":0.04938769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06752517926557867,"score_gpt":0.3967653649223084,"score_spread":0.3292401856567297,"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."}}