{"id":"W4240265726","doi":"10.31525/ct1-nct04140084","title":"Wiki Head CT Choice Study: Adaptation of US Two Decision Aids to a Québec Local Context","year":2019,"lang":"en","type":"article","venue":"Case Medical Research","topic":"Healthcare Systems and Technology","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Adaptation (eye); Head (geology); Decision aids; Psychology; Computer science; Geography; Medicine; Neuroscience; Archaeology; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"medium","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002455001,0.0004640653,0.0004911197,0.001595995,0.00362732,0.002936914,0.001640701,0.001381802,0.02181659],"category_scores_gemma":[0.01556964,0.0003677511,0.0004317389,0.002709521,0.001360783,0.001856741,0.001688025,0.001430524,0.002183306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0193522,"about_ca_system_score_gemma":0.01043328,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8236023,"about_ca_topic_score_gemma":0.9309981,"domain_scores_codex":[0.9983947,0.0008945246,0.00007318048,0.000187544,0.0002579643,0.0001921336],"domain_scores_gemma":[0.98522,0.009286516,0.0003533973,0.001060885,0.003160199,0.0009191039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.009640156,0.01783296,0.2244106,0.001586262,0.0003415881,0.009071793,0.1458139,0.02744497,0.007860483,0.02060369,0.1339587,0.4014349],"study_design_scores_gemma":[0.002092523,0.003611499,0.3861203,0.0007944637,0.0003572759,0.001107715,0.2033864,0.06352654,0.01108387,0.007860566,0.3190665,0.000992337],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9539169,0.0001155644,0.002341259,0.0009940316,0.00005863721,0.001016603,0.003660814,0.0002539572,0.03764215],"genre_scores_gemma":[0.9559894,0.0001458313,0.009796095,0.0004450337,0.00002526627,0.001101196,0.002545535,0.0001830876,0.02976852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1763977,"threshold_uncertainty_score":0.3548731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1098810154859732,"score_gpt":0.436568029331608,"score_spread":0.3266870138456348,"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."}}