{"id":"W4377987130","doi":"10.2196/42149","title":"Dashboard With Bump Charts to Visualize the Changes in the Rankings of Leading Causes of Death According to Two Lists: National Population-Based Time-Series Cross-Sectional Study","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Autopsy Techniques and Outcomes","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Dashboard; Bar chart; Population; Health statistics; Medicine; Public health; Chart; Ranking (information retrieval); Demography; Gerontology; Computer science; Statistics; Environmental health; Data science; Information retrieval; Mathematics","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":[0.002489933,0.000114028,0.0003409843,0.0002719381,0.0001670106,0.00004831192,0.0001186907,0.0000314529,0.00001275687],"category_scores_gemma":[0.0004511573,0.00006666495,0.00002776464,0.0008720087,0.00003906664,0.00006960258,0.00003761529,0.0001054377,0.000001757107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008209962,"about_ca_system_score_gemma":0.0002843709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006398254,"about_ca_topic_score_gemma":0.001014283,"domain_scores_codex":[0.9984729,0.0001524772,0.0003537899,0.0002158754,0.0005375911,0.0002673856],"domain_scores_gemma":[0.9989646,0.0003888567,0.0001498261,0.0001736047,0.0002148026,0.00010833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001512851,0.00005434336,0.996286,0.0001046796,0.00001119381,0.000001871163,0.002316041,0.00002566861,0.00002244185,0.0004527388,0.0002146584,0.0003590166],"study_design_scores_gemma":[0.0006056701,0.0005732664,0.9961243,0.00003867136,5.884656e-7,0.000006789646,0.0008467017,0.000135449,0.00001013704,0.00001875022,0.001572717,0.00006696034],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842807,0.00002674709,0.000073975,0.0141457,0.00003524429,0.001264719,0.00002803865,0.00006820149,0.00007666385],"genre_scores_gemma":[0.9962231,0.000007667035,0.0001379592,0.003117112,0.00007643527,0.0002337498,0.00004221843,0.00001331613,0.000148422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01194241,"threshold_uncertainty_score":0.2718518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06393517271032277,"score_gpt":0.4123964968292293,"score_spread":0.3484613241189065,"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."}}