{"id":"W2471323074","doi":"10.1017/cem.2016.194","title":"P018: A prospective diagnostic support tool for the differentiation of abdominal pain in the adult emergency department population","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Emergency department; Triage; Medicine; Artificial intelligence; Medical diagnosis; Machine learning; Decision tree; Ranking (information retrieval); Support vector machine; Population; Computer science; Medical emergency; Radiology","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005252464,0.0001093066,0.0002597292,0.0001961297,0.0004062615,9.597669e-7,0.0002254467,0.00009004928,0.003312116],"category_scores_gemma":[0.01524221,0.00004725408,0.00007740437,0.0002438176,0.00004291662,0.0001236249,0.000005258321,0.0002806605,0.000006804045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002564094,"about_ca_system_score_gemma":0.0006481368,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004165529,"about_ca_topic_score_gemma":0.04804586,"domain_scores_codex":[0.9969838,0.0004501253,0.001731562,0.00008502053,0.0003785215,0.0003710107],"domain_scores_gemma":[0.9962918,0.001669754,0.0009682982,0.0001707225,0.0006619983,0.0002373962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001540863,0.00002762974,0.8116183,0.0003863336,0.00003687663,0.000002833237,0.01095386,0.000005411315,0.00002353849,0.003713496,0.1477993,0.02527837],"study_design_scores_gemma":[0.0016939,0.001041779,0.9703689,0.0009126989,0.0001010891,0.000002042447,0.002944424,0.0002182063,0.000003158599,0.003273346,0.01935585,0.0000846207],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8333499,0.001093551,0.05537596,0.08778236,0.01706197,0.004402432,0.0001095304,0.000009673161,0.0008146206],"genre_scores_gemma":[0.9972073,0.0006667785,0.0000488875,0.0004685773,0.001180281,0.0001654847,0.00002060635,0.000008723143,0.0002333967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1638574,"threshold_uncertainty_score":0.997599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1591421821981351,"score_gpt":0.434282376219384,"score_spread":0.2751401940212489,"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."}}