{"id":"W4294243752","doi":"10.23889/ijpds.v7i3.1826","title":"Impact of the COVID-19 pandemic on skin cancer diagnosis: A population-based study.","year":2022,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Medicine; Pandemic; Skin cancer; Biopsy; Poisson regression; Cancer registry; Population; Cancer; Cohort; Cohort study; Melanoma; Dermatology; Coronavirus disease 2019 (COVID-19); Demography; Internal medicine; Disease; Environmental health; Infectious disease (medical specialty)","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":[0.0007680419,0.0004750496,0.0002371613,0.0006379106,0.001007559,0.0009646661,0.0007110826,0.000646134,0.002487266],"category_scores_gemma":[0.001938514,0.0005417448,0.0007787644,0.00100649,0.0004978295,0.0006982227,0.0009108016,0.0009587965,0.0004203884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002270459,"about_ca_system_score_gemma":0.001653213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2364354,"about_ca_topic_score_gemma":0.2895716,"domain_scores_codex":[0.9992586,0.0001158285,0.00005351114,0.0001770979,0.0001754743,0.0002195591],"domain_scores_gemma":[0.9988583,0.0001016031,0.0005323904,0.0001040311,0.0001563803,0.0002473047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005710865,0.00004815804,0.9989941,0.000008600075,0.00004017597,0.00003644001,0.00008559634,0.00001228176,0.00006992288,0.000008293683,0.0001746,0.0004647767],"study_design_scores_gemma":[0.00000718178,0.00007135966,0.9993035,0.000004914931,0.0000171823,0.00006712307,0.0002541777,0.00007515628,0.00001416923,0.000006330619,0.0001754834,0.000003501679],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973035,0.000217336,0.00006327896,0.00007567355,0.000008041888,0.00003868759,0.001736018,0.000004797828,0.0005527638],"genre_scores_gemma":[0.9981097,0.0001297706,0.00006713821,0.00007293576,0.00001565273,0.00003431342,0.001098684,0.000002988151,0.0004687933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2364354,"threshold_uncertainty_score":0.4701183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2482335446101796,"score_gpt":0.550354455012218,"score_spread":0.3021209104020384,"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."}}