{"id":"W2177945213","doi":"10.1371/journal.pone.0143480","title":"Merging Children’s Oncology Group Data with an External Administrative Database Using Indirect Patient Identifiers: A Report from the Children’s Oncology Group","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Hospital for Sick Children; SickKids Foundation","funders":"National Cancer Institute; National Institute on Alcohol Abuse and Alcoholism; National Institutes of Health; Children’s Oncology Group","keywords":"Medicine; Clinical Oncology; Identifier; Internal medicine; Oncology; Group (periodic table); Bioinformatics; Computer science; Cancer; Biology; Computer network","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":[0.0836207,0.0007850208,0.0008000615,0.004995534,0.0009017385,0.002935553,0.0021585,0.000909292,0.001712727],"category_scores_gemma":[0.1946642,0.0005768786,0.001697798,0.01192351,0.0009823004,0.002295381,0.003301335,0.001623881,0.0006064149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005022272,"about_ca_system_score_gemma":0.01199501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01519327,"about_ca_topic_score_gemma":0.01355733,"domain_scores_codex":[0.909551,0.04230321,0.01254355,0.00451229,0.02971177,0.001378101],"domain_scores_gemma":[0.8027818,0.08665495,0.04988639,0.02611035,0.03250543,0.002061125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001053387,0.0001278711,0.7598834,0.001405581,0.001556173,0.0001876054,0.001542828,0.003753736,0.001639413,0.003926381,0.02263314,0.2022905],"study_design_scores_gemma":[0.001149731,0.001315009,0.6658607,0.003390742,0.002745681,0.001338303,0.00156313,0.01770557,0.03355252,0.002655511,0.2684654,0.0002576327],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6614153,0.02177813,0.1832113,0.02609599,0.001284805,0.01313961,0.05453847,0.002518282,0.03601803],"genre_scores_gemma":[0.696665,0.005853179,0.24472,0.004403192,0.0005227249,0.005699116,0.03918634,0.0005210532,0.00242925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0836207,"threshold_uncertainty_score":0.4422338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3239754833740492,"score_gpt":0.4535989327935673,"score_spread":0.1296234494195181,"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."}}