{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002802093,0.0003716603,0.0003961288,0.002839391,0.000586019,0.001676413,0.0005198016,0.0003067966,0.01258992],"category_scores_gemma":[0.006107546,0.0002504006,0.0005837815,0.0022407,0.0004027715,0.0008720405,0.000875047,0.0007710371,0.00247353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007715079,"about_ca_system_score_gemma":0.00160503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004090317,"about_ca_topic_score_gemma":0.005602601,"domain_scores_codex":[0.9971246,0.0007089357,0.0006050303,0.0003416052,0.0009880414,0.0002318468],"domain_scores_gemma":[0.9969872,0.0009289524,0.0006763566,0.0001841853,0.001109678,0.0001136369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008787873,0.0005680951,0.6352445,0.003185072,0.0003553907,0.002305062,0.007789471,0.0004369243,0.007569629,0.003845515,0.03317732,0.3046443],"study_design_scores_gemma":[0.00006827197,0.0008492602,0.8117045,0.00274241,0.000381165,0.005202365,0.02553559,0.002478015,0.01529883,0.003161662,0.1324427,0.000135283],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8882954,0.007908449,0.01994616,0.0033919,0.001008311,0.001838707,0.01629928,0.0003532555,0.06095859],"genre_scores_gemma":[0.9129309,0.006676629,0.03842278,0.0009109835,0.0001957398,0.001423403,0.01135401,0.00009505452,0.02799039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01258992,"threshold_uncertainty_score":0.04211754,"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."}}