{"id":"W4396977352","doi":"10.3390/curroncol31050207","title":"Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians","year":2024,"lang":"en","type":"review","venue":"Current Oncology","topic":"Cardiac, Anesthesia and Surgical Outcomes","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Perioperative; Medicine; Narrative review; Patient care; Narrative; Perioperative nursing; Intensive care medicine; MEDLINE; Medical physics; Medical education; Artificial intelligence; Nursing; Surgery; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.00256282,0.0007133887,0.001487614,0.002878236,0.0002954173,0.001539,0.0008830122,0.001153299,0.002675608],"category_scores_gemma":[0.01120884,0.0003086551,0.001557068,0.002689694,0.0004599261,0.00150219,0.0007650911,0.001559975,0.000561257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008643764,"about_ca_system_score_gemma":0.003239261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002129227,"about_ca_topic_score_gemma":0.00484074,"domain_scores_codex":[0.9990966,0.0003152039,0.000250102,0.0001104194,0.0001957313,0.00003194394],"domain_scores_gemma":[0.9926415,0.006126069,0.0004927084,0.00007523008,0.0005938595,0.0000705049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009097049,0.00003376183,0.0006794551,0.1659976,0.0007608096,0.0001364365,0.0002093073,0.0004734006,0.0002525315,0.004104812,0.02146767,0.8057934],"study_design_scores_gemma":[0.00004184413,0.000210699,0.003023587,0.3060907,0.003580161,0.001265926,0.0003416571,0.0004319623,0.0003088634,0.004217699,0.6804169,0.00006998591],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005850232,0.9990513,0.0000815348,0.0004465134,0.00009012342,0.000007010654,0.00002336007,0.000001876799,0.000239774],"genre_scores_gemma":[0.0007051466,0.9987274,0.0001741721,0.0002298877,0.00008731963,0.00001054978,0.00001954597,7.675916e-7,0.00004522322],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002878236,"threshold_uncertainty_score":0.01355368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08540288803139111,"score_gpt":0.4788082472541771,"score_spread":0.393405359222786,"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."}}