{"id":"W3209171793","doi":"10.1177/15330338211050767","title":"Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm","year":2021,"lang":"en","type":"article","venue":"Technology in Cancer Research & Treatment","topic":"Pancreatic and Hepatic Oncology Research","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Pancreaticoduodenectomy; Medicine; Algorithm; Pancreatic cancer; Retrospective cohort study; Cohort; Adenocarcinoma; Machine learning; Pancreatic head; Artificial intelligence; Cancer; Surgery; Internal medicine; Resection; Computer science","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":[0.0007253526,0.000203676,0.0008084782,0.0006881889,0.0001413746,0.0000158825,0.0001792466,0.0002107995,0.0002236672],"category_scores_gemma":[0.0007524884,0.0001204342,0.00005216719,0.002209384,0.0008772596,0.00005283931,0.0001634137,0.001215984,0.000008506332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001520026,"about_ca_system_score_gemma":0.001605144,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02130425,"about_ca_topic_score_gemma":0.0192897,"domain_scores_codex":[0.9972664,0.0004227645,0.000439865,0.0004840457,0.0005680356,0.0008188942],"domain_scores_gemma":[0.9976795,0.00118794,0.00009305192,0.000555983,0.0003882322,0.00009524983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002969491,0.0007173347,0.9137924,0.0000935384,0.0002573434,0.001009998,0.000504204,0.00007160164,0.001295015,0.000152842,0.00002196312,0.08178677],"study_design_scores_gemma":[0.01379941,0.006680812,0.9267817,0.002418308,0.0004593206,0.0003639444,0.003604599,0.02081276,0.01299985,0.0004343523,0.01126677,0.0003781472],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9624289,0.008077099,0.00005390302,0.02774803,0.00001399607,0.001443371,0.00003016492,0.0000506045,0.0001538802],"genre_scores_gemma":[0.9800691,0.007812836,0.005545243,0.00006969459,0.0000295503,0.00388004,0.00001935364,0.00003450494,0.002539689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08140862,"threshold_uncertainty_score":0.9986057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2038617164271218,"score_gpt":0.4785597273621083,"score_spread":0.2746980109349865,"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."}}