{"id":"W4415174374","doi":"10.64857/emviro.v3i2.43","title":"Analisa Ketepatan Kode Diagnosis Berdasarkan ICD-10 dengan Penerapan Karakter Ke-4 pada 10 Besar Penyakit Tribulan IV","year":2024,"lang":"en","type":"article","venue":"Emviro Jurnal Ilmiah Penelitian Kesehatan","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Population; Officer; Coding (social sciences); Diagnosis code; Medical record; Data collection; Quarter (Canadian coin)","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":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003662443,0.0008001315,0.001061771,0.0007880948,0.002630517,0.0002946107,0.001043962,0.0008462137,0.03191143],"category_scores_gemma":[0.0008705169,0.0007059203,0.0004228355,0.001025579,0.0002521326,0.001345566,0.0003594247,0.003160988,0.007956481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009430947,"about_ca_system_score_gemma":0.001289699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001555215,"about_ca_topic_score_gemma":0.001291247,"domain_scores_codex":[0.9910873,0.001372314,0.002566828,0.001071247,0.001639051,0.002263253],"domain_scores_gemma":[0.9944034,0.001643626,0.0005465032,0.001203052,0.0003993399,0.00180405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008069323,0.0004304961,0.1067909,0.005315701,0.0004450407,0.0008534217,0.01515222,0.00004252178,0.0005271403,0.008886206,0.8170149,0.04373457],"study_design_scores_gemma":[0.002274332,0.0006157159,0.05520103,0.003257249,0.000253597,0.00007146673,0.002963149,0.003788581,0.0003339769,0.001021104,0.9290218,0.001198002],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8810177,0.00744982,0.001024895,0.0240934,0.006596972,0.003257818,0.002034552,0.001619166,0.07290571],"genre_scores_gemma":[0.9601252,0.001103556,0.0005013602,0.0049415,0.004711773,0.0007117595,0.001555015,0.0001867956,0.02616305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1120069,"threshold_uncertainty_score":0.9995392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1504572266472118,"score_gpt":0.4297149487685267,"score_spread":0.2792577221213149,"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."}}