{"id":"W3001400935","doi":"10.2196/16080","title":"Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; Intermountain Healthcare","keywords":"Asthma; Medicine; Medical emergency; Intensive care medicine; Pediatrics; Internal medicine","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":[0.0005360897,0.0001984624,0.0004145043,0.000336102,0.00007943016,0.0001146949,0.00120905,0.0001510037,0.00002329864],"category_scores_gemma":[0.0009613949,0.000180642,0.000122828,0.001522787,0.0000321806,0.0006816931,0.0004803951,0.0004920107,0.00003530911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001755721,"about_ca_system_score_gemma":0.0005854814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001352229,"about_ca_topic_score_gemma":0.00002646919,"domain_scores_codex":[0.9972006,0.00005213816,0.001050618,0.0002341342,0.0009955311,0.0004669674],"domain_scores_gemma":[0.9984747,0.0001967661,0.0002237742,0.0003390367,0.0001676872,0.0005980397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009281196,0.0004118357,0.09249862,0.005408851,0.0004797024,0.0000280922,0.424007,0.09788259,5.240611e-7,0.02048235,0.01446305,0.3442446],"study_design_scores_gemma":[0.0006291926,0.0003311227,0.005947792,0.0001122625,0.000006367636,7.046032e-7,0.000531859,0.9898246,0.000001356742,0.0002018075,0.002198815,0.0002141488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1539866,0.000005484008,0.8365411,0.00810329,0.0001228211,0.0007859286,0.00001746366,0.0001410952,0.0002962411],"genre_scores_gemma":[0.6534196,0.000003053501,0.3269847,0.01917232,0.00005846306,0.0002756855,0.00006221771,0.00001403556,0.000009988826],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.891942,"threshold_uncertainty_score":0.7366366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617513048227109,"score_gpt":0.2989055907700519,"score_spread":0.2827304602877808,"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."}}