{"id":"W2914462514","doi":"10.2196/13030","title":"Achieving Rapid Blood Pressure Control With Digital Therapeutics: Retrospective Cohort and Machine Learning Study","year":2019,"lang":"en","type":"article","venue":"JMIR Cardio","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Retrospective cohort study; Control (management); Medicine; Cohort; Blood pressure; Computer science; Artificial intelligence; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003809444,0.0005043477,0.0006073206,0.0006768926,0.00139079,0.001068252,0.0007903459,0.0008723277,0.001895905],"category_scores_gemma":[0.007863429,0.0007081972,0.001010641,0.0008029833,0.0006712436,0.001223312,0.0009847539,0.001985049,0.0007956966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006382184,"about_ca_system_score_gemma":0.001531777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004536248,"about_ca_topic_score_gemma":0.004871731,"domain_scores_codex":[0.9980982,0.000508625,0.0002100232,0.0005039171,0.0004318205,0.0002474657],"domain_scores_gemma":[0.99563,0.000922014,0.0008968016,0.001154801,0.0008131613,0.0005832175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001236865,0.001875948,0.9904479,0.00005285895,0.0001959776,0.0002640756,0.000676082,0.0001040871,0.0002884062,0.0001367964,0.001131293,0.003589847],"study_design_scores_gemma":[0.0004032685,0.00693336,0.983071,0.00007751969,0.000309777,0.001731887,0.002362263,0.001627757,0.000336317,0.000256703,0.002825061,0.00006521577],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972498,0.0001547663,0.0008815098,0.00008679886,0.0000243996,0.0003499702,0.0007937766,0.00001154406,0.0004474237],"genre_scores_gemma":[0.9962703,0.0001670263,0.001085137,0.0002107113,0.00004894849,0.0004441953,0.001333275,0.00001341152,0.0004269693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004536248,"threshold_uncertainty_score":0.02014649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618817886135154,"score_gpt":0.3328059048448794,"score_spread":0.3166177259835278,"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."}}