{"id":"W7026799484","doi":"","title":"Association of Fall-Related Injuries and Different Diagnoses in Older Adults of Ontario: A Machine Learning Approach","year":2023,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Christian Theology and Mission","field":"Arts and Humanities","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medical diagnosis; Diagnosis code; Decision tree; Gradient boosting; Random forest; Poison control; Boosting (machine learning); Injury prevention; Medical record","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002519744,0.0001545481,0.000310643,0.0004405105,0.0001344601,0.00003570786,0.0001598875,0.0001403993,0.00008569205],"category_scores_gemma":[0.00006272135,0.0001501877,0.00006401859,0.0001227504,0.000139799,0.0003795815,0.000175524,0.0004130876,0.000009207482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001439343,"about_ca_system_score_gemma":0.00004156261,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009397088,"about_ca_topic_score_gemma":0.1734149,"domain_scores_codex":[0.9989575,0.0001969387,0.0002457091,0.0002361634,0.0001616776,0.0002020341],"domain_scores_gemma":[0.99933,0.0001611185,0.0002608764,0.0001190742,0.00007472008,0.00005415994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001474041,0.0001331002,0.9578546,0.0001212539,0.00006542807,0.00001549934,0.04057087,0.00001222039,0.00003897333,0.0008336297,8.394899e-7,0.0002061327],"study_design_scores_gemma":[0.001446794,0.0001174556,0.9945642,0.0003177913,0.00006151228,0.00000112272,0.002257421,0.000002383034,0.0002434838,0.0001767502,0.0006614896,0.0001495713],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988574,0.0001031651,0.000003653862,0.00005083418,0.0001023175,0.0001735847,0.00002380767,0.0000596762,0.0006255404],"genre_scores_gemma":[0.9681439,0.00007164157,0.000001852183,0.00001033301,0.00001340178,9.923235e-7,0.00006655048,0.00001287714,0.03167846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1724752,"threshold_uncertainty_score":0.8416681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03908861518338129,"score_gpt":0.2513762587564717,"score_spread":0.2122876435730904,"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."}}