{"id":"W4376956970","doi":"10.7554/elife.86527.sa0","title":"Editor's evaluation: Quantification of impact of COVID-19 pandemic on cancer screening programmes – a case study from Argentina, Bangladesh, Colombia, Morocco, Sri Lanka, and Thailand","year":2023,"lang":"en","type":"peer-review","venue":"","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Sri lanka; Pandemic; Coronavirus disease 2019 (COVID-19); Geography; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Socioeconomics; Medicine; Virology; Sociology; Environmental planning; Outbreak; Pathology; Disease; Infectious disease (medical specialty); Tanzania","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.02546941,0.00069378,0.0008431632,0.002453594,0.002323827,0.005581762,0.002391361,0.006208196,0.01570302],"category_scores_gemma":[0.1758116,0.0003473862,0.0007508933,0.002639815,0.00198107,0.002579438,0.002563408,0.00309479,0.001509916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005599995,"about_ca_system_score_gemma":0.03010336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02497606,"about_ca_topic_score_gemma":0.03827785,"domain_scores_codex":[0.9817616,0.008542606,0.002045859,0.000706668,0.005993631,0.0009495854],"domain_scores_gemma":[0.8619677,0.06013142,0.01129097,0.001904136,0.05622891,0.008476758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006446699,0.00004710998,0.007114658,0.002058481,0.0000858889,0.001418911,0.0009375553,0.0002434381,0.00007548715,0.001337886,0.9607964,0.02581962],"study_design_scores_gemma":[0.0001443089,0.000131907,0.01787884,0.009518343,0.0002550748,0.001809035,0.008010519,0.001198097,0.0004081705,0.001440443,0.9591141,0.00009109992],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"editorial","genre_scores_codex":[0.01169523,0.01091025,0.0006955169,0.8706134,0.08847394,0.0005613778,0.001609924,0.0000923488,0.01534807],"genre_scores_gemma":[0.3009953,0.04987467,0.006655226,0.4125162,0.1660274,0.001573026,0.001425176,0.000420244,0.06051282],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.02546941,"threshold_uncertainty_score":0.1346967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2857140019777394,"score_gpt":0.5275235802075442,"score_spread":0.2418095782298048,"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."}}