{"id":"W4413770379","doi":"10.2196/82923","title":"Videos on Bilibili, TikTok, and Xiaohongshu as Sources of Medical Information for Adenoid Hypertrophy: A Cross-Sectional Content Analysis (Preprint)","year":2025,"lang":"en","type":"preprint","venue":"JMIR Formative Research","topic":"Delphi Technique in Research","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cross-sectional study; Preprint; Adenoid hypertrophy; Environmental health; Medicine; Computer science; Pathology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001568921,0.0001885717,0.0001872091,0.002060569,0.000499612,0.0006215948,0.0002094842,0.000233474,0.009136026],"category_scores_gemma":[0.01042743,0.0001571174,0.0003153031,0.001780352,0.0003240161,0.001137731,0.0007454231,0.0003875881,0.0007166635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008971171,"about_ca_system_score_gemma":0.000899473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004327824,"about_ca_topic_score_gemma":0.005820696,"domain_scores_codex":[0.999482,0.0001497505,0.00008397158,0.00007423524,0.0001445059,0.00006549038],"domain_scores_gemma":[0.9908254,0.004544389,0.002008906,0.0001956067,0.001867871,0.000557893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001269475,0.0008398188,0.8216044,0.00211904,0.0001364056,0.0005872793,0.04109716,0.0002098076,0.005060053,0.0005298459,0.01248673,0.11406],"study_design_scores_gemma":[0.00001982578,0.0004138091,0.9617155,0.0002251395,0.00006370168,0.0001556392,0.02940689,0.0004067635,0.001379952,0.00007340876,0.006106177,0.00003326389],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99532,0.0001424456,0.0003718109,0.0001674392,0.00002140746,0.000313699,0.001986995,0.00002203679,0.001654286],"genre_scores_gemma":[0.9880226,0.0004578256,0.002655142,0.0002483826,0.00004594108,0.001245732,0.003693305,0.00002236928,0.003608825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009136026,"threshold_uncertainty_score":0.03056306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2359982643100925,"score_gpt":0.5438038797300925,"score_spread":0.30780561542,"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."}}