{"id":"W4412755233","doi":"10.1111/jdv.20883","title":"Analysis of global skin cancer epidemiology in 2022 and correlation with dermatologist density","year":2025,"lang":"en","type":"letter","venue":"Journal of the European Academy of Dermatology and Venereology","topic":"Nonmelanoma Skin Cancer Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Future Earth","funders":"","keywords":"Medicine; Dermatology; Epidemiology; Skin cancer; Cancer; Pathology; 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":[0.001345519,0.0003185943,0.0002582094,0.003349697,0.0002320297,0.0009366517,0.0003817855,0.0003725271,0.002290821],"category_scores_gemma":[0.004635646,0.000186224,0.0009171771,0.005390509,0.0001956036,0.000797727,0.0008580184,0.0004860439,0.0005566202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008045066,"about_ca_system_score_gemma":0.0006899915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02228036,"about_ca_topic_score_gemma":0.01906263,"domain_scores_codex":[0.9993302,0.0001837278,0.00009631115,0.0001198884,0.0001648973,0.0001051604],"domain_scores_gemma":[0.9972878,0.0003705899,0.001345962,0.0001687311,0.0005653862,0.0002616495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003153151,0.00000917251,0.9952353,0.00004743549,0.00009942721,0.00002926513,0.00005021265,0.0003018174,0.00003402623,0.000082329,0.001008328,0.003071166],"study_design_scores_gemma":[0.000001878794,0.00002386426,0.9972363,0.00002668759,0.00002318684,0.0001231952,0.0001793986,0.0005199506,0.00003703647,0.00004865497,0.00177514,0.000004777184],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.938092,0.005667957,0.001496764,0.001206072,0.00009858571,0.00007888639,0.04792745,0.0001161597,0.005316091],"genre_scores_gemma":[0.9732632,0.00168087,0.0008971375,0.0001969423,0.00009088088,0.00009128784,0.02303383,0.00002444827,0.0007215969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02228036,"threshold_uncertainty_score":0.04430133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02230200142202629,"score_gpt":0.3145252074423581,"score_spread":0.2922232060203318,"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."}}