{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01942915,0.0008463847,0.002552064,0.204628,0.001703588,0.005384409,0.001471024,0.0007772637,0.003743519],"category_scores_gemma":[0.08033261,0.0003627576,0.002854263,0.2415049,0.001302544,0.003997207,0.004470593,0.0006360991,0.0006884765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003081284,"about_ca_system_score_gemma":0.007484685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004565784,"about_ca_topic_score_gemma":0.004555147,"domain_scores_codex":[0.9808807,0.005785021,0.003967892,0.001253928,0.007438148,0.0006741879],"domain_scores_gemma":[0.907743,0.06439426,0.01253611,0.002981117,0.01147545,0.0008700683],"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":[0.0005403494,0.0005516294,0.3853029,0.05538056,0.00321424,0.001024659,0.02737344,0.003510594,0.003434328,0.01162532,0.02060005,0.487442],"study_design_scores_gemma":[0.0002114062,0.0005053149,0.7657898,0.01013751,0.004019669,0.002005998,0.05615087,0.02038788,0.004976316,0.01829824,0.1171917,0.000325276],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7975168,0.02374788,0.01862369,0.003473826,0.0002937976,0.008032663,0.1112655,0.0009100776,0.03613584],"genre_scores_gemma":[0.8851066,0.01431274,0.05362822,0.0002661243,0.0004387794,0.009127858,0.03477468,0.0001940476,0.002150782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.795372,"threshold_uncertainty_score":0.1027524,"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."}}