{"id":"W4416786473","doi":"10.5770/cgj.28.909","title":"Notice of Retraction: Predicting falls among older persons using machine learning [abstract]","year":2025,"lang":"en","type":"article","venue":"Canadian Geriatrics Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Notice; Poison control; Injury prevention; Older people; Human factors and ergonomics; MEDLINE","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"},{"model":"gpt","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001152354,0.000166614,0.0002741289,0.001048418,0.000641293,0.0004744232,0.0005444451,0.0001921214,0.00007083067],"category_scores_gemma":[0.0005256485,0.000188473,0.0001490892,0.001149254,0.00004041365,0.001086847,0.00006737531,0.001094419,0.000006454639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006127497,"about_ca_system_score_gemma":0.00156773,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02737245,"about_ca_topic_score_gemma":0.02458717,"domain_scores_codex":[0.9982066,0.0001665797,0.0005455771,0.0002828718,0.0003545948,0.0004438247],"domain_scores_gemma":[0.9980209,0.000287535,0.0005416277,0.0002584771,0.0004385587,0.000452929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001943054,0.0001235498,0.8799551,0.000275902,0.0004851131,0.0007362441,0.006305492,0.005492921,0.003173463,0.0007365821,0.003003787,0.09969238],"study_design_scores_gemma":[0.003029339,0.0001930998,0.4852102,0.001479127,0.0003520506,0.003631421,0.004450086,0.4633707,0.001748293,0.000348888,0.03466901,0.001517785],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.753077,0.0007854247,0.2327097,0.001093415,0.005241417,0.0002896812,0.00002879609,0.0000930663,0.006681477],"genre_scores_gemma":[0.9957536,0.00002122264,0.003317499,0.0001077137,0.0003440551,0.000001766382,0.000001843017,0.00001374249,0.0004385886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4578778,"threshold_uncertainty_score":0.9932116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02213921780509946,"score_gpt":0.2498575830068983,"score_spread":0.2277183652017989,"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."}}