{"id":"W4248000657","doi":"10.22374/cjgim.v7i4.129","title":"Patient Profiling","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of General Internal Medicine","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Profiling (computer programming); Intensive care medicine; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001196435,0.0001475416,0.0003511842,0.0003859798,0.0003651299,0.000004620006,0.0002563801,0.0001429657,0.002909902],"category_scores_gemma":[0.0012962,0.0001061146,0.00007453794,0.0001759194,0.00016028,0.0002163128,0.000021612,0.001095213,0.0002040149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006293425,"about_ca_system_score_gemma":0.001285047,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07227854,"about_ca_topic_score_gemma":0.03172672,"domain_scores_codex":[0.997136,0.0003456045,0.001255392,0.00009623668,0.0003074632,0.0008593189],"domain_scores_gemma":[0.9962147,0.0002327519,0.0006126196,0.00017083,0.0007463795,0.002022718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006936639,0.00001488763,0.8639768,0.000085551,0.00005173603,0.0001623254,0.0191382,0.00002644488,0.001877006,0.005734632,0.09039226,0.01847079],"study_design_scores_gemma":[0.001119898,0.002835463,0.04441737,0.006013344,0.0001597852,0.0009796541,0.05578049,0.000863286,0.01022317,0.004377974,0.8725083,0.0007212653],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9562775,0.004262161,0.0005019465,0.006413024,0.01417175,0.000274806,0.000009473072,0.000009436515,0.01807985],"genre_scores_gemma":[0.9841709,0.00003937846,0.000839183,0.004747865,0.008487812,0.000007539574,0.0000027787,0.00002616444,0.001678389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8195595,"threshold_uncertainty_score":0.9980016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1610206795481054,"score_gpt":0.4624802827362806,"score_spread":0.3014596031881751,"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."}}