{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007917038,0.0005252481,0.0002402149,0.001297759,0.0007278673,0.001564203,0.001035918,0.0007957565,0.005111115],"category_scores_gemma":[0.03052896,0.0002066255,0.0005633426,0.0005874226,0.000626796,0.00143343,0.002248519,0.0008673936,0.0009461507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001026888,"about_ca_system_score_gemma":0.003876611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001596525,"about_ca_topic_score_gemma":0.00270746,"domain_scores_codex":[0.9932382,0.004725459,0.0004333916,0.0003251947,0.0007908185,0.0004870019],"domain_scores_gemma":[0.9751481,0.01682929,0.002719563,0.001884148,0.002198524,0.00122035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008208581,0.004976461,0.1042092,0.001917146,0.0002044036,0.0003422497,0.006223366,0.003205627,0.005871449,0.004070516,0.012715,0.8554437],"study_design_scores_gemma":[0.002323133,0.03656177,0.4892074,0.01293516,0.001285704,0.001657515,0.04298217,0.07679056,0.06892707,0.02980615,0.2368104,0.000713003],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8056877,0.001709669,0.1255399,0.01795645,0.0005422469,0.008040449,0.001647385,0.002756502,0.03611967],"genre_scores_gemma":[0.9110529,0.0008764555,0.08120129,0.001457529,0.000148695,0.002755309,0.0004241472,0.00003701237,0.002046713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007917038,"threshold_uncertainty_score":0.04186976,"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."}}