{"id":"W3216282190","doi":"10.1002/ijc.33884","title":"Predicted long‐term impact of <scp>COVID</scp> ‐19 pandemic‐related care delays on cancer mortality in Canada","year":2021,"lang":"en","type":"article","venue":"International Journal of Cancer","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":134,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Partnership Against Cancer; McGill University; McGill University Health Centre","funders":"Partenariat Canadien Contre Le Cancer; Compute Canada; Canada Foundation for Innovation; Ministère de l'Économie, de la Science et de l'Innovation - Québec; Canadian Institutes of Health Research; Ministère de la Santé; Ministère de la Santé et des Services sociaux; Cancer Care Ontario","keywords":"Medicine; Pandemic; Cancer; Incidence (geometry); Cancer registry; Lung cancer; Colorectal cancer; Coronavirus disease 2019 (COVID-19); Demography; Disease; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001974549,0.000176942,0.0004896224,0.0002535452,0.00002332196,0.00001568821,0.0002184713,0.0001150069,0.0006318585],"category_scores_gemma":[0.0007548829,0.0001445954,0.0002284214,0.0002737894,0.00003801135,0.0001204514,0.00003851771,0.0005880609,6.485868e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.01056554,"about_ca_system_score_gemma":0.03524953,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7830195,"about_ca_topic_score_gemma":0.8997496,"domain_scores_codex":[0.9974632,0.00008163147,0.0008505245,0.0001877151,0.001136598,0.00028036],"domain_scores_gemma":[0.9966915,0.0002790525,0.0006077039,0.0001686763,0.001798029,0.0004550921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002873155,0.00008326325,0.9808672,0.0001346532,0.001041567,0.001498149,0.001226463,0.005375715,0.001034913,0.000007460834,0.001404872,0.007038407],"study_design_scores_gemma":[0.003240036,0.0002049969,0.9930185,0.001140821,0.0001422021,0.0002505906,0.0002362409,0.0001339019,0.0006364563,0.00004009804,0.0009010226,0.00005511742],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899494,0.006730097,0.00002246845,0.0009473258,0.001372652,0.0001417476,0.0005667681,0.000007746939,0.0002617839],"genre_scores_gemma":[0.9943441,0.002992093,0.000008923522,0.002029789,0.0004435494,0.000007248388,0.00004836299,0.00002016854,0.000105749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1167301,"threshold_uncertainty_score":0.9932327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.058641473950114,"score_gpt":0.4460827180956057,"score_spread":0.3874412441454917,"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."}}