{"id":"W2742035694","doi":"10.1017/dmp.2017.52","title":"Diagnostic Imaging in Disasters: A Bibliometric Analysis","year":2017,"lang":"en","type":"review","venue":"Disaster Medicine and Public Health Preparedness","topic":"Disaster Response and Management","field":"Health Professions","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver General Hospital","funders":"","keywords":"Preparedness; Medicine; Emergency management; Disaster medicine; MEDLINE; Public health; Terrorism; China; Outbreak; Medical emergency; Poison control; Suicide prevention; Political science; Pathology","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01363972,0.001368549,0.004358381,0.2158249,0.00181884,0.005341057,0.001478934,0.00112503,0.005561894],"category_scores_gemma":[0.1116194,0.0006228046,0.005354827,0.2116085,0.001244502,0.004516142,0.003144931,0.000626972,0.0006791523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003597412,"about_ca_system_score_gemma":0.007311266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006031778,"about_ca_topic_score_gemma":0.006448698,"domain_scores_codex":[0.9793072,0.004644698,0.006836143,0.001836668,0.006768573,0.0006067937],"domain_scores_gemma":[0.8608014,0.1008496,0.02146503,0.002164751,0.01348638,0.001232802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001401179,0.0002944303,0.4874467,0.2082209,0.02031765,0.001522151,0.003288968,0.002925585,0.001669584,0.002716777,0.0173955,0.2528005],"study_design_scores_gemma":[0.0006057146,0.0007664149,0.7952616,0.05672533,0.04572893,0.004985167,0.009052766,0.00852897,0.001723732,0.007589426,0.06863723,0.0003946425],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4250444,0.424302,0.005560216,0.005884181,0.0004847392,0.003901899,0.1160006,0.0006057426,0.0182163],"genre_scores_gemma":[0.8136922,0.1436509,0.01251122,0.0004397372,0.0005542992,0.003164418,0.02483927,0.0001136698,0.001034377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7841751,"threshold_uncertainty_score":0.07213461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4103893026117218,"score_gpt":0.569077636771543,"score_spread":0.1586883341598212,"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."}}