{"id":"W2890048049","doi":"10.23889/ijpds.v3i4.657","title":"Data Linkage for Optimizing Rectal Cancer Care in Alberta","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Clinical practice guidelines implementation","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services","funders":"","keywords":"Medicine; Colorectal cancer; Grading (engineering); Multidisciplinary approach; Family medicine; Linkage (software); Data extraction; MEDLINE; Cancer; Medical physics; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.1113411,0.0009391369,0.001000566,0.01552441,0.00379814,0.008180405,0.005772239,0.001201546,0.006897688],"category_scores_gemma":[0.2098435,0.0008777032,0.001595424,0.03092051,0.001296871,0.002323637,0.009245112,0.001734602,0.0008732234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0764338,"about_ca_system_score_gemma":0.2428726,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8576022,"about_ca_topic_score_gemma":0.8141962,"domain_scores_codex":[0.9090445,0.039733,0.01008495,0.005613727,0.03162013,0.003903731],"domain_scores_gemma":[0.7323014,0.06113163,0.02513657,0.01863109,0.1526411,0.01015809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008422565,0.000244583,0.2585039,0.004849503,0.001143544,0.000295939,0.005111902,0.01172102,0.0007876349,0.02140544,0.2161547,0.4789396],"study_design_scores_gemma":[0.0006832798,0.000389107,0.4183932,0.009049899,0.001159691,0.0002282203,0.008406786,0.03183167,0.003229075,0.01522255,0.5110768,0.0003297269],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1876298,0.03399456,0.150133,0.1908413,0.003585325,0.02168018,0.2405766,0.009985076,0.1615742],"genre_scores_gemma":[0.4429598,0.01127107,0.383127,0.009392119,0.0009281161,0.01059687,0.1256748,0.0008407246,0.01520941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1423978,"threshold_uncertainty_score":0.5888353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4398875122444226,"score_gpt":0.6094285896483009,"score_spread":0.1695410774038783,"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."}}