{"id":"W4366957172","doi":"10.2196/45531","title":"Improving Kidney Outcomes in Patients With Nondiabetic Chronic Kidney Disease Through an Artificial Intelligence–Based Health Coaching Mobile App: Retrospective Cohort Study","year":2023,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; eHealth; Kidney disease; Health coaching; Retrospective cohort study; Intervention (counseling); Health care; Physical therapy; mHealth; Psychological intervention; Internal medicine; Nursing","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":[0.001115928,0.0005184801,0.0008057,0.0009231375,0.00159601,0.001312841,0.0005987176,0.000859479,0.002539786],"category_scores_gemma":[0.003232111,0.0007115277,0.001854126,0.00142829,0.0004094213,0.001143417,0.00106045,0.001772986,0.0006449306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007208521,"about_ca_system_score_gemma":0.00111391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01049347,"about_ca_topic_score_gemma":0.01186243,"domain_scores_codex":[0.9988462,0.0001853152,0.0001667256,0.0003654334,0.0002261922,0.0002101149],"domain_scores_gemma":[0.9983079,0.0002273507,0.0005859557,0.0002196756,0.0004161405,0.0002428625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008842151,0.0007106477,0.9945629,0.0001399464,0.0003474874,0.0001606105,0.0004253449,0.00002603449,0.0001667566,0.00003432386,0.0004230642,0.002118725],"study_design_scores_gemma":[0.000244257,0.001978255,0.9928936,0.0001123931,0.0007960619,0.0004827681,0.00151895,0.000384288,0.0001379538,0.00008197307,0.001326764,0.00004267263],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978136,0.0004683707,0.0001761488,0.00004280706,0.00001615454,0.0002395377,0.0008305566,0.000004594128,0.0004081995],"genre_scores_gemma":[0.9970267,0.0004065537,0.0003328163,0.0002089145,0.00003516057,0.0004259025,0.001130028,0.000007860212,0.000425942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01049347,"threshold_uncertainty_score":0.02086478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04603719309494378,"score_gpt":0.4264322561616912,"score_spread":0.3803950630667474,"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."}}