{"id":"W2969528045","doi":"10.1089/sur.2019.151","title":"Implementing Mobile Health Interventions to Capture Post-Operative Patient-Generated Health Data","year":2019,"lang":"en","type":"article","venue":"Surgical Infections","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Centers for Disease Control and Prevention","keywords":"Medicine; Health care; Psychological intervention; Incentive; Stakeholder; Process management; Process (computing); Health information technology; Legislation; Knowledge management; Nursing; Business; Public relations","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002508545,0.0003474706,0.000795519,0.0003800955,0.003946092,0.00004086918,0.0004532874,0.0002172156,0.006310611],"category_scores_gemma":[0.0002095224,0.0003191702,0.0001868506,0.001345646,0.00004791847,0.0003238409,0.0008497959,0.001342252,0.004008033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007593564,"about_ca_system_score_gemma":0.002948403,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01084529,"about_ca_topic_score_gemma":0.01660335,"domain_scores_codex":[0.9927652,0.001572195,0.002150753,0.001068477,0.0003684028,0.002074908],"domain_scores_gemma":[0.9948354,0.0006050473,0.0007224498,0.001649762,0.0005967858,0.001590553],"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":[0.0002560684,0.00447861,0.07948467,0.006537531,0.0002690392,0.00001219843,0.01669117,0.001580094,0.0002204131,0.06758617,0.5257594,0.2971246],"study_design_scores_gemma":[0.001067369,0.001445348,0.002101857,0.000466498,0.00001228866,0.00001403098,0.004997205,0.0002443137,0.000003124345,0.00005204614,0.9893258,0.0002701287],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7198496,0.007847462,0.01220021,0.09243164,0.0108794,0.09903977,0.01688266,0.002858409,0.03801086],"genre_scores_gemma":[0.9107307,0.001088474,0.002332545,0.03208989,0.0008475509,0.03597517,0.01002953,0.0001496625,0.006756496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4635664,"threshold_uncertainty_score":0.999926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07778345292191119,"score_gpt":0.4954607973679608,"score_spread":0.4176773444460496,"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."}}