{"id":"W2319398543","doi":"10.1177/155989770700700705","title":"STITCH: Simplified Treatment Algorithm Leads to Improved Blood Pressure Control","year":2007,"lang":"en","type":"article","venue":"MD Conference Express","topic":"Cardiovascular Health and Risk Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Blood pressure; Algorithm; Medicine; Control (management); Mathematics; Computer science; Control theory (sociology); Intensive care medicine; Internal medicine; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0009095858,0.0004935424,0.001550767,0.0007303749,0.0002636228,0.000772553,0.0004828483,0.0006649414,0.006294437],"category_scores_gemma":[0.003083383,0.0001879717,0.001461518,0.0006182566,0.0003408306,0.0005652326,0.0003867537,0.001517136,0.0008201631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006029434,"about_ca_system_score_gemma":0.001075691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001555471,"about_ca_topic_score_gemma":0.002882762,"domain_scores_codex":[0.9989655,0.0003963181,0.0001343788,0.0001346697,0.0002874592,0.00008171664],"domain_scores_gemma":[0.9992127,0.0002557598,0.0002070213,0.00007879284,0.00007178688,0.0001738077],"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":[0.07549297,0.005129288,0.009034085,0.001552175,0.002149063,0.0001983987,0.0001060003,0.01052626,0.00495358,0.003885569,0.03699503,0.8499775],"study_design_scores_gemma":[0.3478203,0.1024045,0.2397833,0.001781839,0.01351152,0.003993423,0.0002607299,0.143217,0.01154094,0.03569568,0.09930773,0.0006829452],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.818426,0.02599944,0.07783106,0.02069089,0.003619869,0.005257079,0.005410652,0.004449726,0.0383152],"genre_scores_gemma":[0.9115977,0.005063219,0.06849777,0.005670103,0.001504543,0.001135461,0.002623431,0.0001411868,0.003766531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006294437,"threshold_uncertainty_score":0.02105695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02043303291884951,"score_gpt":0.2903588252697991,"score_spread":0.2699257923509496,"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."}}