{"id":"W4413751223","doi":"10.2196/77214","title":"Social Media–Based Cancer Education: Bibliometric and Thematic Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Thematic map; Thematic analysis; Sociology; Qualitative research; Social science; Geography; Computer science; World Wide Web; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004655235,0.00009825689,0.0002466573,0.009634664,0.000639282,0.00009387305,0.0001791332,0.0001404576,0.001040454],"category_scores_gemma":[0.001174458,0.0001004995,0.00007898827,0.07683875,0.0002400445,0.0001337675,0.0000174325,0.000124536,0.000006761072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006598455,"about_ca_system_score_gemma":0.003968198,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01463066,"about_ca_topic_score_gemma":0.02201854,"domain_scores_codex":[0.9986266,0.0001847396,0.0002320023,0.0002442734,0.0004233524,0.0002890351],"domain_scores_gemma":[0.9977407,0.00155916,0.000147079,0.0001205272,0.0003012337,0.0001312669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001184146,0.0001550814,0.4082954,0.0002078918,0.0001792275,1.437043e-7,0.04220819,0.000006064494,0.000006879719,0.008692408,0.02645118,0.5137857],"study_design_scores_gemma":[0.0002220295,0.000004397115,0.8812132,0.00008857882,0.000433817,1.283838e-8,0.00752488,0.00005903102,0.00002218477,0.002051178,0.108206,0.0001746346],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9023448,0.01218937,0.00007043179,0.0647958,0.008320825,0.001773444,0.0000219559,0.0001341602,0.01034928],"genre_scores_gemma":[0.9870344,0.00123366,0.0001429809,0.001462403,0.002002046,0.007608547,0.000007515383,0.000009076418,0.000499338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.513611,"threshold_uncertainty_score":0.9998727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1203781607279827,"score_gpt":0.5308964582902044,"score_spread":0.4105182975622216,"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."}}