{"id":"W2733469716","doi":"10.1093/geroni/igx004.3320","title":"DEVELOPING A SIMPLE ALGORITHM TO PREDICT FALLS IN PRIMARY CARE OLDER PATIENTS","year":2017,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"CHAID; Odds; Odds ratio; Medicine; Poison control; Fear of falling; Depression (economics); Gait; Balance (ability); Injury prevention; Demography; Algorithm; Physical therapy; Logistic regression; Gerontology; Machine learning; Medical emergency; Internal medicine; Computer science; Decision tree","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":[0.003019732,0.001178174,0.001513771,0.002325489,0.0008794278,0.001968229,0.001596226,0.001544547,0.002782078],"category_scores_gemma":[0.01028196,0.0005766077,0.001258358,0.001753011,0.000247706,0.001151414,0.0009394071,0.001343917,0.001656151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077322,"about_ca_system_score_gemma":0.002730469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01257685,"about_ca_topic_score_gemma":0.009820838,"domain_scores_codex":[0.9986014,0.0003878772,0.0002192252,0.0003891618,0.0002542368,0.0001480941],"domain_scores_gemma":[0.9963241,0.001981071,0.0003157816,0.000107835,0.001126726,0.0001444326],"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":[0.0007624028,0.0008642068,0.1471303,0.0004403054,0.000616972,0.0004039967,0.0003837606,0.1262084,0.001795824,0.002547771,0.02274976,0.6960963],"study_design_scores_gemma":[0.0001991508,0.0002023688,0.01042983,0.0001001532,0.0001571331,0.0003148352,0.0001607778,0.978463,0.0008655916,0.005913274,0.003156522,0.00003731403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1287533,0.001103183,0.8547375,0.001789702,0.0003303114,0.001584548,0.002969702,0.005856188,0.002875434],"genre_scores_gemma":[0.3421994,0.000514919,0.648097,0.000470469,0.000214038,0.001532102,0.005125199,0.0001100723,0.001736854],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01257685,"threshold_uncertainty_score":0.02500731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03456602809474317,"score_gpt":0.3746091256798494,"score_spread":0.3400430975851063,"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."}}