{"id":"W3160458018","doi":"10.1097/mph.0000000000002199","title":"Impact of COVID-19 Pandemic on Timing of Childhood Cancer Diagnoses","year":2021,"lang":"en","type":"article","venue":"Journal of Pediatric Hematology/Oncology","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pediatric Oncology Group","funders":"","keywords":"Medicine; Sequela; Pandemic; Medical diagnosis; Coronavirus disease 2019 (COVID-19); Childhood cancer; Cancer; Health care; 2019-20 coronavirus outbreak; Blood cancer; Pediatric cancer; Pediatrics; Family medicine; Intensive care medicine; Medical emergency; Disease; Surgery; Outbreak; Pathology; Internal medicine; Infectious disease (medical specialty)","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":[0.001064054,0.0001357876,0.0001849407,0.0009022837,0.0008187001,0.000987591,0.000384632,0.0004609305,0.004643186],"category_scores_gemma":[0.008360473,0.0001202814,0.0002436541,0.001326529,0.0002769911,0.0007234011,0.001073527,0.001207638,0.0003597236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002744889,"about_ca_system_score_gemma":0.003096634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07935124,"about_ca_topic_score_gemma":0.1070623,"domain_scores_codex":[0.9985568,0.000393334,0.000145584,0.0001902743,0.0002940123,0.0004199604],"domain_scores_gemma":[0.9953087,0.0007685502,0.002104719,0.0001302989,0.0009298226,0.0007578674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001546233,0.00005918677,0.9294044,0.0001468172,0.00005242682,0.0005772015,0.00090246,0.0006644753,0.0003239667,0.001628703,0.02319416,0.04289159],"study_design_scores_gemma":[0.000007539569,0.00008524541,0.9612339,0.0003661442,0.00003528467,0.0008372803,0.003488964,0.0005662931,0.000394858,0.0005540401,0.03240119,0.00002918046],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8627638,0.01068297,0.001217228,0.05653454,0.001157102,0.0001381482,0.02061604,0.0001706954,0.04671958],"genre_scores_gemma":[0.9881484,0.003814395,0.0006734249,0.002944291,0.0002827564,0.0000338639,0.003037785,0.00002627214,0.00103895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07935124,"threshold_uncertainty_score":0.1577787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1248385063812358,"score_gpt":0.4929987690378586,"score_spread":0.3681602626566227,"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."}}