{"id":"W4400986784","doi":"10.21037/mhealth-23-20","title":"The evolution and mapping trends of mobile health (m-Health): a bibliometric analysis (1997–2023)","year":2024,"lang":"en","type":"article","venue":"mHealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Geography; Environmental health; Computer science; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"not_applicable","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","sts"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.008326109,0.0003104081,0.001005793,0.03950908,0.00352897,0.00004430492,0.0003425374,0.0002419885,0.0001918634],"category_scores_gemma":[0.0001917449,0.0002343346,0.0002193352,0.1628901,0.0001880789,0.0001573456,0.0001509544,0.001149209,0.000124797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001590175,"about_ca_system_score_gemma":0.00549369,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01260688,"about_ca_topic_score_gemma":0.004737461,"domain_scores_codex":[0.9927055,0.001382692,0.00260817,0.0008273559,0.0006619248,0.00181434],"domain_scores_gemma":[0.994435,0.002091374,0.001065857,0.000919605,0.0002483677,0.00123983],"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":[0.0000346911,0.00009082059,0.01493834,0.005074419,0.00016854,8.701199e-7,0.001967411,0.00002644524,0.000004267554,0.02865332,0.06736255,0.8816783],"study_design_scores_gemma":[0.0004738942,0.000568102,0.1905084,0.0004109609,0.00009176334,0.000004562761,0.003222253,0.005105088,3.580917e-7,0.001029168,0.798407,0.0001784643],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.09764517,0.7064121,0.04445187,0.113767,0.005479259,0.01903031,0.002108697,0.001626166,0.009479417],"genre_scores_gemma":[0.9240054,0.05374625,0.001119683,0.003678316,0.0005452841,0.01238559,0.0002842943,0.00007241064,0.00416275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8814999,"threshold_uncertainty_score":0.9977683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05901089199329632,"score_gpt":0.4536944523763699,"score_spread":0.3946835603830736,"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."}}