{"id":"W2331671813","doi":"10.1097/hp.0b013e3181c64f90","title":"MEDECOR—A MEDICAL DECORPORATION TOOL TO ASSIST FIRST RESPONDERS, RECEIVERS, AND MEDICAL REACH-BACK PERSONNEL IN TRIAGE, TREATMENT, AND RISK ASSESSMENT AFTER INTERNALIZATION OF RADIONUCLIDES","year":2010,"lang":"en","type":"article","venue":"Health Physics","topic":"Disaster Response and Management","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Triage; Internalization; Radionuclide; Risk assessment; Medicine; Medical physics; Medical emergency; Environmental health; Computer science; Computer security; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002234417,0.000186287,0.0004262203,0.0001615048,0.0002687992,0.00001840877,0.0001158843,0.0001993677,0.0004722161],"category_scores_gemma":[0.0005647799,0.0001580139,0.00004029236,0.0002410216,0.00009494719,0.000192412,0.0001336455,0.000474929,0.00002384236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002465598,"about_ca_system_score_gemma":0.0007132484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001949163,"about_ca_topic_score_gemma":0.01743438,"domain_scores_codex":[0.9971536,0.0008875749,0.0007449662,0.000379695,0.0004826534,0.0003515339],"domain_scores_gemma":[0.9982169,0.0006025274,0.0003931671,0.0002911513,0.00006998509,0.0004262944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003129989,0.001178865,0.6444157,0.001316546,0.0001497059,0.00004302094,0.05122339,0.000006105739,0.00003378254,0.0125323,0.0131271,0.2728435],"study_design_scores_gemma":[0.01925766,0.002572776,0.7686636,0.003297067,0.0001537852,0.00001160111,0.01488006,0.007506755,0.00002610791,0.005019361,0.177817,0.0007942992],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743856,0.00009252293,0.01666621,0.006079487,0.0004799199,0.001394241,0.00004362633,0.00002748713,0.0008308714],"genre_scores_gemma":[0.9902,0.002981164,0.002663275,0.002844996,0.0003058026,0.0002842662,0.00006476396,0.00003342174,0.000622259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2720492,"threshold_uncertainty_score":0.9728791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04149075235444922,"score_gpt":0.4235170015487176,"score_spread":0.3820262491942684,"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."}}