{"id":"W2889799971","doi":"10.23889/ijpds.v3i4.925","title":"Cancer Screening in the Toronto Central LHIN by Sub-region and Neighbourhood: Evidence from an Applied Health Research Question (AHRQ)","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Medicine; Family medicine; Immigration; Cancer screening; Psychological intervention; Cervical cancer; Health care; Population; Cancer; Cervical cancer screening; Neighbourhood (mathematics); Demography; Gerontology; Environmental health; Nursing; Geography; Internal medicine; Political science","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":[0.01456405,0.0003508583,0.000709258,0.002016235,0.001799786,0.001915895,0.001965983,0.0008809932,0.004259731],"category_scores_gemma":[0.06015086,0.0005406538,0.001783733,0.006210956,0.001925958,0.0008267314,0.001837588,0.0006254945,0.000229187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.020535,"about_ca_system_score_gemma":0.03323732,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8949247,"about_ca_topic_score_gemma":0.9193106,"domain_scores_codex":[0.9837127,0.007843184,0.001886012,0.001212999,0.004085467,0.001259648],"domain_scores_gemma":[0.9234207,0.03503817,0.02110817,0.002598249,0.01473373,0.003100996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008442852,0.00009524378,0.9426444,0.01478218,0.001729432,0.0000893629,0.003248726,0.000207435,0.00006807013,0.001041528,0.01457217,0.02067732],"study_design_scores_gemma":[0.0002211603,0.000147328,0.9814931,0.006433409,0.001396918,0.00004786015,0.002688116,0.0002269161,0.00007161401,0.0001877701,0.00705658,0.00002936915],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7270377,0.1616144,0.001133028,0.02036398,0.0006368129,0.002083411,0.06734697,0.00005327273,0.01973044],"genre_scores_gemma":[0.9819348,0.01059054,0.000511962,0.001551789,0.0001313114,0.0006393645,0.004236839,0.000007738355,0.0003956936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1050753,"threshold_uncertainty_score":0.2113882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3894744167739918,"score_gpt":0.554729586927895,"score_spread":0.1652551701539032,"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."}}