{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007040265,0.0004044508,0.0004277843,0.001261482,0.0001451563,0.0006404668,0.0002640829,0.000250895,0.0007023642],"category_scores_gemma":[0.003025172,0.0001698471,0.000259745,0.001141199,0.0002483809,0.0004175149,0.000365952,0.0002811779,0.0003000002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001801625,"about_ca_system_score_gemma":0.0002479588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005405263,"about_ca_topic_score_gemma":0.0006779879,"domain_scores_codex":[0.9995381,0.0001591971,0.00006510981,0.00009116271,0.0001179556,0.00002862662],"domain_scores_gemma":[0.9988153,0.0005728606,0.0002954857,0.00007272711,0.0001899602,0.00005372645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001337596,0.0002020312,0.7751678,0.0002849486,0.00007319904,0.000750348,0.0006157414,0.002148675,0.03964504,0.0002217182,0.0006701404,0.1788828],"study_design_scores_gemma":[0.0000605658,0.001733005,0.876308,0.0001055,0.0001561136,0.006549196,0.001282744,0.05708684,0.05266457,0.0008366601,0.003142974,0.00007384221],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9695671,0.0009081874,0.02717453,0.0001240391,0.00003261939,0.00008916936,0.0003756799,0.0003377104,0.001390991],"genre_scores_gemma":[0.9780486,0.0004894718,0.02069445,0.00004829221,0.00002370663,0.0000577954,0.0002052534,0.0000201424,0.0004124373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001261482,"threshold_uncertainty_score":0.003723264,"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."}}