{"id":"W2971794993","doi":"10.2196/14849","title":"The Service of Research Analytics to Optimize Digital Health Evidence Generation: Multilevel Case Study","year":2019,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; Institute for Work & Health; University of Waterloo; Women's College Hospital; Hospital for Sick Children; Public Health Ontario; SickKids Foundation; University of Toronto; University Health Network","funders":"","keywords":"Digital health; Analytics; Computer science; Data science; Service (business); Health care; Business; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08433038,0.000555812,0.0006263684,0.002873917,0.01389995,0.009040826,0.003596301,0.003687228,0.005959862],"category_scores_gemma":[0.09352928,0.0007650768,0.001401922,0.00325112,0.01133699,0.007953646,0.01570608,0.004122294,0.0008381535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01793459,"about_ca_system_score_gemma":0.0375572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0192566,"about_ca_topic_score_gemma":0.04182799,"domain_scores_codex":[0.9004526,0.08736614,0.002444322,0.002140201,0.004052411,0.003544381],"domain_scores_gemma":[0.8572145,0.105763,0.007931608,0.01177574,0.008742477,0.008572683],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0007259701,0.003386095,0.09052266,0.003133953,0.000250448,0.01495966,0.5462559,0.005163434,0.003521114,0.1295655,0.01586073,0.1866545],"study_design_scores_gemma":[0.0006580523,0.003180927,0.0291347,0.005850068,0.0004048448,0.005220012,0.6613992,0.0190821,0.006266727,0.07027156,0.1981844,0.0003472894],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7420465,0.002608965,0.1135816,0.07026476,0.0003368635,0.011278,0.0007185404,0.0003128479,0.05885196],"genre_scores_gemma":[0.8772452,0.001394368,0.1108458,0.002837325,0.0001043958,0.004645898,0.0001181603,0.00006799225,0.002740797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9156696,"threshold_uncertainty_score":0.445987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5094411487083393,"score_gpt":0.6550278038453701,"score_spread":0.1455866551370307,"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."}}