{"id":"W4247261076","doi":"10.2196/preprints.18212","title":"Theme Trends and Knowledge Structure on Mobile Health Apps: Bibliometric Analysis (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"mHealth; The Internet; Digital health; Library science; World Wide Web; Popularity; Internet privacy; Health care; Computer science; Medicine; Political science; Psychological intervention; Nursing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006881819,0.0004762294,0.001798008,0.1193949,0.001133667,0.005504133,0.000755631,0.0006664241,0.008615213],"category_scores_gemma":[0.06242988,0.0002732508,0.002340642,0.194975,0.0006142409,0.004412972,0.002423886,0.0004892673,0.001489517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002531961,"about_ca_system_score_gemma":0.004514355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007665374,"about_ca_topic_score_gemma":0.006755985,"domain_scores_codex":[0.9907803,0.001871313,0.002346739,0.001063816,0.003462329,0.000475574],"domain_scores_gemma":[0.921622,0.05739782,0.009563616,0.001455554,0.009266865,0.0006941405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004764361,0.0002011157,0.6074017,0.03320008,0.003188899,0.0006079641,0.0101781,0.003248617,0.002252549,0.007078215,0.04717933,0.2849869],"study_design_scores_gemma":[0.00008550996,0.0001740085,0.87512,0.004981188,0.00232519,0.0009213813,0.01333238,0.008865804,0.001922498,0.005218259,0.08692036,0.0001333426],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6703538,0.04036863,0.005436632,0.005225454,0.0003551655,0.001232439,0.2486163,0.0005834782,0.0278281],"genre_scores_gemma":[0.8749982,0.02711015,0.01064547,0.000306813,0.0006551901,0.001992244,0.07946676,0.0001598205,0.004665446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8806051,"threshold_uncertainty_score":0.03639495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08209180804089848,"score_gpt":0.4723015082248014,"score_spread":0.3902097001839029,"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."}}