{"id":"W3115251323","doi":"10.21203/rs.3.rs-78755/v1","title":"An Evaluation of the Patient Clinical Complexity Level (PCCL) Method for the Complexity Adjustment in the Korean Diagnosis-Related Groups (KDRG)","year":2020,"lang":"en","type":"preprint","venue":"Research Square (Research Square)","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Reimbursement; Resource consumption; Payment; Payment by Results; Resource (disambiguation); Statistics; Psychology; Computer science; Mathematics; Ecology; Biology; Health care; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch","sts","research_integrity"],"category_scores_codex":[0.236362,0.0005681575,0.001304372,0.0007181604,0.005286125,0.0001507458,0.004545665,0.001410346,0.00107792],"category_scores_gemma":[0.04804793,0.0003033399,0.000659845,0.002480351,0.002937406,0.0002388558,0.003497675,0.01948229,0.0001505754],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002375023,"about_ca_system_score_gemma":0.01077985,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.014151,"about_ca_topic_score_gemma":0.01045278,"domain_scores_codex":[0.8651546,0.1124776,0.004440233,0.001427079,0.01368786,0.002812667],"domain_scores_gemma":[0.9290656,0.05602276,0.001307013,0.003696483,0.008986484,0.0009216339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003155838,0.005088955,0.05464602,0.01995022,0.0005393125,0.00001005865,0.1475738,0.003130879,0.00002090227,0.1835921,0.2389949,0.3432971],"study_design_scores_gemma":[0.003430581,0.002097741,0.4367803,0.003245556,0.0001176164,0.000001434609,0.03565118,0.3663138,0.00002097301,0.1394879,0.01249854,0.0003543519],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4254721,0.00471514,0.01962658,0.3842495,0.006587618,0.1459531,0.006195232,0.0003513803,0.006849375],"genre_scores_gemma":[0.9766747,0.001314682,0.00198542,0.001625671,0.001282611,0.01579655,0.001176904,0.00008838886,0.00005502176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5512027,"threshold_uncertainty_score":0.9999419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8908125011607185,"score_gpt":0.6813190841213117,"score_spread":0.2094934170394068,"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."}}