{"id":"W2592440681","doi":"10.2196/mhealth.6259","title":"“Back on Track”: A Mobile App Observational Study Using Apple’s ResearchKit Framework","year":2017,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Mobile apps; Observational study; Track (disk drive); Mobile device; Open source; World Wide Web; Internet privacy; Medicine; Software","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.008914758,0.0006612993,0.001139848,0.001645428,0.002891203,0.001902382,0.001021624,0.001358926,0.003007906],"category_scores_gemma":[0.02052206,0.001023605,0.001192822,0.0008568048,0.001210464,0.002588472,0.002170989,0.00280088,0.001684819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001341337,"about_ca_system_score_gemma":0.00315049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00502596,"about_ca_topic_score_gemma":0.008455932,"domain_scores_codex":[0.9959319,0.002133833,0.0004629757,0.0004904238,0.0005060451,0.000474757],"domain_scores_gemma":[0.9888388,0.004281216,0.001717275,0.001214411,0.002581817,0.001366624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003820688,0.03567247,0.5256559,0.002629865,0.0004131032,0.006621613,0.2798511,0.0004570438,0.003396656,0.001099548,0.0137501,0.126632],"study_design_scores_gemma":[0.001219589,0.04310209,0.6521094,0.00182305,0.0005329464,0.003092624,0.2486063,0.002384031,0.00290977,0.001402813,0.04225416,0.0005631763],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926019,0.0001953378,0.001406711,0.0003336688,0.0000431423,0.003487023,0.0006499109,0.00004379303,0.001238393],"genre_scores_gemma":[0.973342,0.0004644739,0.007167483,0.001298522,0.0001091883,0.01353172,0.0009187181,0.00006940879,0.003098373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008914758,"threshold_uncertainty_score":0.04714626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3898239411485765,"score_gpt":0.5808212496885181,"score_spread":0.1909973085399416,"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."}}