{"id":"W4231590464","doi":"10.1186/isrctn41238563","title":"Effects of remote patient monitoring on chronic disease management","year":2016,"lang":"en","type":"dataset","venue":"http://isrctn.com/","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Disease management; Computer science; Disease monitoring; Medicine; Disease; Intensive care medicine; Medical emergency; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001430236,0.001339053,0.0009631311,0.001109157,0.0003574034,0.0007837464,0.001514851,0.00147414,0.006869918],"category_scores_gemma":[0.005840816,0.0002657484,0.001457144,0.001516552,0.0002523002,0.0006979067,0.0009882997,0.00111097,0.005184514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001321919,"about_ca_system_score_gemma":0.001154216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05050991,"about_ca_topic_score_gemma":0.1042701,"domain_scores_codex":[0.999155,0.000223518,0.00009877254,0.0002249003,0.0001841873,0.0001136896],"domain_scores_gemma":[0.9983505,0.0005390392,0.0002198241,0.0003499157,0.0003159004,0.0002248976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001425924,0.00042585,0.04479006,0.0008453364,0.000361614,0.0001129541,0.00006210575,0.00587988,0.0003976298,0.0004248965,0.9244012,0.02087243],"study_design_scores_gemma":[0.003133403,0.001002179,0.3888747,0.001306792,0.001185829,0.00102511,0.0006895762,0.04405464,0.003661669,0.003031976,0.5517203,0.000313813],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02172324,0.0007428215,0.0002426508,0.001190163,0.0002818975,0.00004723779,0.9735238,0.0004257715,0.001822393],"genre_scores_gemma":[0.02302885,0.0002732456,0.0005651916,0.0001872037,0.000056024,0.00007221434,0.9743507,0.00002617828,0.001440475],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05050991,"threshold_uncertainty_score":0.1004318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07078984182660018,"score_gpt":0.43577747516599,"score_spread":0.3649876333393898,"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."}}