{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005672643,0.0008044004,0.001022797,0.0004714611,0.0008542298,0.00001740436,0.001032994,0.0007913664,0.000588158],"category_scores_gemma":[0.0006087223,0.0006531629,0.0003256975,0.0003357274,0.0001670823,0.0001193877,0.0008670081,0.001824035,0.004737645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002032114,"about_ca_system_score_gemma":0.0006818086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001567333,"about_ca_topic_score_gemma":0.0002429484,"domain_scores_codex":[0.9931579,0.001118049,0.00181939,0.001153166,0.001229555,0.001521966],"domain_scores_gemma":[0.9935308,0.001709357,0.001304627,0.002380452,0.0003205939,0.0007541418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003731603,0.0002230275,0.0003263411,0.02046617,0.0001326529,0.0002972774,0.0002625143,0.00001505811,0.00001842456,0.0002417815,0.8992566,0.07838692],"study_design_scores_gemma":[0.000425024,0.0007094212,0.0006823766,0.02597054,0.0002507429,4.793837e-7,0.0002723667,0.00008592847,0.0004717362,0.0006437423,0.9698225,0.0006651568],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00628975,0.003749442,0.0001141164,0.001320213,0.01665162,0.008062755,0.962807,0.0002639163,0.0007412093],"genre_scores_gemma":[0.03384057,0.02905223,0.0004459275,0.002440176,0.01236619,0.002971524,0.9098347,0.000705854,0.008342803],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07772176,"threshold_uncertainty_score":0.9995919,"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."}}