{"id":"W4310947449","doi":"10.2196/42245","title":"How TikTok Is Being Used to Help Individuals Cope With Breast Cancer: Cross-sectional Content Analysis","year":2022,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Breast cancer; Content analysis; Coping (psychology); Breast cancer awareness; Social support; Cross-sectional study; Popularity; Medicine; Social media; Cancer; Psychology; Family medicine; Clinical psychology; Social psychology; Internal medicine; Pathology; Computer science; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006370031,0.0001334936,0.0002628358,0.0002443782,0.00161292,0.0002690665,0.0003231186,0.00007435389,0.004871185],"category_scores_gemma":[0.0001022054,0.0001349271,0.0001020639,0.002483393,0.0001648814,0.0002549225,0.0000696303,0.0002473008,0.00001053003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00249914,"about_ca_system_score_gemma":0.001685471,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07576221,"about_ca_topic_score_gemma":0.04007803,"domain_scores_codex":[0.9973317,0.0002303146,0.0002267495,0.0004284367,0.00126879,0.0005139488],"domain_scores_gemma":[0.9986398,0.0002864695,0.0002256525,0.0001799022,0.0003662752,0.0003019413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003944885,0.00004541883,0.9450951,0.00001094518,0.000190342,8.04765e-7,0.04823586,0.0002449285,0.00002428618,0.0002637368,0.002413407,0.003435711],"study_design_scores_gemma":[0.0002538458,0.00003914776,0.8791301,0.00001020697,0.00007544281,4.98064e-7,0.007540147,0.000009829859,0.00003042592,0.00003343091,0.1127047,0.0001722117],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9513803,0.0001858375,0.00001769933,0.04320429,0.002713217,0.001577001,0.0005761907,0.00007140681,0.0002740556],"genre_scores_gemma":[0.9791086,0.00004776303,0.00006292632,0.002335766,0.001669953,0.01473449,0.00002350614,0.00002107463,0.001995934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1102913,"threshold_uncertainty_score":0.9996868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1558125607857403,"score_gpt":0.4401848720767463,"score_spread":0.284372311291006,"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."}}