{"id":"W4412702991","doi":"10.1371/journal.pone.0325072","title":"Intelligent surgical drainage - digitizing the analysis of drainage fluid in patients with surgical drains","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Clinical Laboratory Practices and Quality Control","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Hemoglobin; Bilirubin; Drainage; Nuclear medicine; Hematocrit; Amylase; Linear regression; Medicine; Biomedical engineering; Chromatography; Surgery; Chemistry; Mathematics; Internal medicine; Statistics; Biology; Biochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.001044816,0.0001615227,0.000900626,0.0002937016,0.00006449231,0.00003703273,0.0001582056,0.0001048195,0.0001950891],"category_scores_gemma":[0.001063861,0.0001002925,0.0001993607,0.001884829,0.0001764176,0.0001037669,0.00009083241,0.0003967769,0.000007468286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007348663,"about_ca_system_score_gemma":0.0001054552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000084385,"about_ca_topic_score_gemma":0.000129873,"domain_scores_codex":[0.9977735,0.0003059189,0.0007332377,0.0003302845,0.0005865726,0.0002705202],"domain_scores_gemma":[0.9966039,0.002257172,0.0001931152,0.0005495982,0.0002855042,0.0001107292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007688762,0.02187068,0.9012915,0.0004926777,0.01309775,0.0004423563,0.001129362,0.00006827134,0.0002152577,0.04602966,0.00004638697,0.007627287],"study_design_scores_gemma":[0.0141497,0.001441584,0.9482045,0.001283652,0.01418699,0.000001254755,0.0007959771,0.005675466,0.0008635863,0.000418296,0.01252029,0.0004587392],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846006,0.0001875018,0.0000320765,0.003714338,0.00001806864,0.0005860503,0.00003951943,0.00002752206,0.01079432],"genre_scores_gemma":[0.9985682,0.00003517099,0.00008703918,0.0005711074,0.00004059517,0.0000221108,0.00006610654,0.00001165586,0.0005980022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04691292,"threshold_uncertainty_score":0.4089809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04427453445128667,"score_gpt":0.3141181564159247,"score_spread":0.2698436219646381,"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."}}