{"id":"W4388423798","doi":"10.2196/46708","title":"Optimal Look-Back Period to Identify True Incident Cases of Diabetes in Medical Insurance Data in the Chinese Population: Retrospective Analysis Study","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Medical Board","keywords":"Medicine; Period (music); Population; Retrospective cohort study; Actuarial science; Demography; Environmental health; Business; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008909738,0.0004838078,0.000656487,0.002021141,0.0007233901,0.0009156373,0.0008569485,0.0005145466,0.0007468179],"category_scores_gemma":[0.01357795,0.0004717314,0.001771662,0.002724651,0.0005471574,0.0009485224,0.0007423084,0.000735309,0.0001076686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001340762,"about_ca_system_score_gemma":0.002181588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0241202,"about_ca_topic_score_gemma":0.01799667,"domain_scores_codex":[0.994714,0.001635341,0.00110397,0.001207836,0.0008338599,0.0005049873],"domain_scores_gemma":[0.9889393,0.002626838,0.004181413,0.001983875,0.001710123,0.0005583275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007706082,0.00001339362,0.9985499,0.00002312451,0.000146201,0.00004283828,0.00009219437,0.00006502205,0.00003738352,0.00003334524,0.00009967752,0.000819853],"study_design_scores_gemma":[0.00002006078,0.0001070077,0.9957748,0.00003314189,0.0004792674,0.0002414029,0.0003777156,0.00216903,0.0001680452,0.0001013528,0.0005128838,0.00001523567],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956708,0.000838346,0.001557586,0.0001236564,0.00002030326,0.0001224761,0.00136576,0.000009684404,0.0002914573],"genre_scores_gemma":[0.9969909,0.0002281192,0.001037791,0.00006513524,0.00002510562,0.0001406785,0.00140198,0.000005530215,0.0001047497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0241202,"threshold_uncertainty_score":0.04795963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05186774930398004,"score_gpt":0.4086737185063521,"score_spread":0.3568059692023721,"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."}}