{"id":"W7115821580","doi":"","title":"DRIVING INTELLIGENT DECISIONS IN HEALTHCARE WITH RECOVERY-AWARE SYSTEM REDESIGN","year":2025,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Train; Baseline (sea); Health care; Domain (mathematical analysis); Value (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02610416,0.001785987,0.001336264,0.003762149,0.001672495,0.009248991,0.002978202,0.004094217,0.003529912],"category_scores_gemma":[0.06162531,0.001049491,0.002658484,0.002128093,0.004409004,0.01033312,0.005406523,0.003662532,0.0009441364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004086832,"about_ca_system_score_gemma":0.01361576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006492722,"about_ca_topic_score_gemma":0.008011415,"domain_scores_codex":[0.9726881,0.01902564,0.002038721,0.002420943,0.002624258,0.001202338],"domain_scores_gemma":[0.9590543,0.0294761,0.00446059,0.003568965,0.002646689,0.0007932861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003524334,0.0009238908,0.02918624,0.01346716,0.001632234,0.0004870919,0.007916288,0.1514203,0.003041202,0.1177683,0.0196338,0.654171],"study_design_scores_gemma":[0.0005953955,0.001991224,0.01251264,0.01513506,0.001964182,0.0005350019,0.01562583,0.2178057,0.01102385,0.5197808,0.2024812,0.0005492227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.092235,0.04420418,0.6885159,0.1234207,0.00151607,0.003419441,0.001227507,0.003828602,0.04163267],"genre_scores_gemma":[0.6266218,0.01600999,0.3467212,0.006732871,0.000314568,0.00118057,0.0005860824,0.0002044346,0.001628525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02610416,"threshold_uncertainty_score":0.1380537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02071948589622487,"score_gpt":0.2545589318550294,"score_spread":0.2338394459588045,"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."}}