{"id":"W4404659884","doi":"10.2196/50627","title":"Application of Dragonnet and Conformal Inference for Estimating Individualized Treatment Effects for Personalized Stroke Prevention: Retrospective Cohort Study","year":2024,"lang":"en","type":"article","venue":"JMIR Cardio","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Inference; Retrospective cohort study; Cohort; Medicine; Computer science; Internal medicine; Artificial intelligence; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000357342,0.000171075,0.0005366879,0.00009471232,0.00005685555,0.00003097334,0.00004660434,0.00005748466,0.000004929756],"category_scores_gemma":[0.00009734168,0.0001427274,0.0001993781,0.00009376116,0.00005040697,0.00007277202,0.00004215239,0.00005783068,0.000001180544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002128877,"about_ca_system_score_gemma":0.00006338633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001771142,"about_ca_topic_score_gemma":0.000001590289,"domain_scores_codex":[0.9989327,0.00002563715,0.0002694108,0.0003508463,0.00025509,0.0001663767],"domain_scores_gemma":[0.9992678,0.0002476107,0.0001013047,0.0002183754,0.0001101581,0.0000547822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001774034,0.0009478622,0.791428,0.005659168,0.01130173,0.00002171044,0.01091838,0.0000473989,0.004172126,0.004220137,0.004853313,0.1646562],"study_design_scores_gemma":[0.04189587,0.01707488,0.7852255,0.001007409,0.0111917,0.00006004175,0.003890962,0.0861891,0.007520331,0.0003644392,0.04479145,0.0007883506],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7162377,0.0004049103,0.2614021,0.00008340498,0.0001955021,0.01928964,0.0002061434,0.0001240125,0.002056652],"genre_scores_gemma":[0.9694821,0.00000885696,0.01903367,0.0000150964,0.0001948758,0.00949282,0.0001784244,0.00002352722,0.001570672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2532444,"threshold_uncertainty_score":0.5820256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01477450757089438,"score_gpt":0.3394825423357343,"score_spread":0.3247080347648399,"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."}}