{"id":"W3015088231","doi":"10.12927/hcq.2020.26144","title":"Connecting Data to Insight: A Pan-Canadian Study on AI in Healthcare","year":2020,"lang":"en","type":"article","venue":"Healthcare Quarterly","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Health Economics","funders":"","keywords":"Health care; Healthcare policy; Public healthcare; Healthcare system; Public relations; Analytics; Health administration; State (computer science); Business; Political science; Data science; Health policy; Health care reform; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.007790294,0.0003450674,0.0006037332,0.003013728,0.01566933,0.008367846,0.001944863,0.0009421339,0.005161596],"category_scores_gemma":[0.02525297,0.0005687919,0.0005607067,0.01221213,0.004993997,0.003114652,0.004709836,0.003522518,0.0003502724],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0968985,"about_ca_system_score_gemma":0.1935777,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9972626,"about_ca_topic_score_gemma":0.9984195,"domain_scores_codex":[0.9931505,0.001202143,0.0002925795,0.0004849094,0.002767526,0.002102376],"domain_scores_gemma":[0.975773,0.004923096,0.002231906,0.0008384093,0.01081536,0.00541834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002288973,0.0002985325,0.4332051,0.0003245899,0.0001158436,0.0004963751,0.4977008,0.0002233375,0.0002985443,0.009325027,0.01877854,0.03900443],"study_design_scores_gemma":[0.00001891785,0.00005485076,0.4175119,0.0004887968,0.0000567912,0.0001795281,0.5435079,0.0004224199,0.00009705128,0.0008730488,0.03668905,0.00009974582],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9390119,0.003106187,0.0003824059,0.01526465,0.00008843064,0.000200947,0.001963794,0.00002446421,0.0399572],"genre_scores_gemma":[0.9900041,0.002742778,0.000382001,0.002126892,0.00001392156,0.00004596308,0.0005381652,0.00002981394,0.004116375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9031015,"threshold_uncertainty_score":0.7030511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3735159501249978,"score_gpt":0.5149587173955668,"score_spread":0.1414427672705689,"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."}}