{"id":"W3088284571","doi":"10.1177/2055668320938604","title":"Health App Review Tool: Matching mobile apps to Alzheimer’s populations (HART Match)","year":2020,"lang":"en","type":"article","venue":"Journal of Rehabilitation and Assistive Technologies Engineering","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centres Intégré Universitaires de Santé et de Services Sociaux; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université Laval","funders":"","keywords":"Computer science; Stakeholder; Matching (statistics); App store; Population; Smartphone app; Mobile apps; Human–computer interaction; Data science; World Wide Web; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.001098836,0.0001812758,0.0006040737,0.0002364469,0.0003736919,0.00001032501,0.0001949438,0.000128281,0.00002693933],"category_scores_gemma":[0.001727326,0.0001599792,0.00009170447,0.0006347657,0.00003719785,0.0001713647,0.0001027099,0.0007891684,0.00003269671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001900296,"about_ca_system_score_gemma":0.0002680996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002174768,"about_ca_topic_score_gemma":0.000007477165,"domain_scores_codex":[0.9976358,0.000121275,0.001362037,0.0002433418,0.0002401835,0.0003973669],"domain_scores_gemma":[0.9978061,0.0006894034,0.0006541166,0.0002181644,0.0003062306,0.0003260187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001500372,0.0001889229,0.01740069,0.02037444,0.0001395595,0.000006078688,0.005748032,0.004494471,0.0007440643,0.04311022,0.1561634,0.75148],"study_design_scores_gemma":[0.001141003,0.001788746,0.0586638,0.008817391,0.0001133012,0.00002320962,0.01440282,0.0009056123,0.00002092188,0.001340534,0.9122928,0.0004898443],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.09474144,0.1133946,0.2262829,0.5479596,0.001206548,0.01370435,0.0001402457,0.002234501,0.0003358199],"genre_scores_gemma":[0.6944547,0.01654533,0.2736169,0.0113376,0.0002922946,0.003620734,0.00002310197,0.00007678573,0.00003256131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7561294,"threshold_uncertainty_score":0.6523762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05160615691945296,"score_gpt":0.3980708080655772,"score_spread":0.3464646511461242,"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."}}