{"id":"W3122140351","doi":"10.2196/25655","title":"Development and Validation of Risk Scores for All-Cause Mortality for a Smartphone-Based “General Health Score” App: Prospective Cohort Study Using the UK Biobank","year":2021,"lang":"en","type":"review","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council","keywords":"Biobank; Metric (unit); Interpretability; Univariate; Population; Medicine; Statistic; Multivariate statistics; Cohort; Statistics; Computer science; Machine learning; Environmental health; Mathematics; Bioinformatics; Operations management; Engineering; Internal medicine","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":[0.02514022,0.0005807265,0.0007067066,0.002302683,0.0005709134,0.00203253,0.001262726,0.00119903,0.00264918],"category_scores_gemma":[0.07923572,0.0003487537,0.001178246,0.001759872,0.0006793684,0.001357454,0.001521378,0.001294578,0.001515678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001100448,"about_ca_system_score_gemma":0.002490535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006267562,"about_ca_topic_score_gemma":0.007882634,"domain_scores_codex":[0.990688,0.004068983,0.001471093,0.00114826,0.002369398,0.0002542794],"domain_scores_gemma":[0.9535372,0.01825246,0.006803862,0.00350397,0.01707036,0.0008321344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002186942,0.002142309,0.7842146,0.003289878,0.001051416,0.0008843464,0.00554679,0.002096475,0.002642598,0.002155771,0.01725736,0.1765315],"study_design_scores_gemma":[0.0005409005,0.005198154,0.9412148,0.00327103,0.001111499,0.001807919,0.003451249,0.01133476,0.003022075,0.00141729,0.02741634,0.0002140656],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.9208685,0.002477978,0.04216051,0.001023612,0.0001932915,0.009995602,0.01722096,0.0003463723,0.005713225],"genre_scores_gemma":[0.8913385,0.002054921,0.07129651,0.0007960839,0.0001191128,0.01329641,0.01866306,0.0001003053,0.002335226],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02514022,"threshold_uncertainty_score":0.1329557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2708114070880317,"score_gpt":0.540985663743695,"score_spread":0.2701742566556634,"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."}}