{"id":"W4226294230","doi":"10.2196/35446","title":"Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"HIV/AIDS Research and Interventions","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Department of Health and Human Services","keywords":"Ethnic group; Social media; Thematic analysis; Medicine; Human immunodeficiency virus (HIV); Sexual orientation; Psychology; Medical education; Artificial intelligence; Family medicine; Computer science; Qualitative research; World Wide Web; Social psychology; Political science","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":[0.002916049,0.000620808,0.0005479136,0.001777358,0.0008205621,0.001100712,0.0006524934,0.0008294876,0.0009532007],"category_scores_gemma":[0.009564837,0.0002483925,0.001023414,0.001197252,0.0005883401,0.001261381,0.0008279643,0.001190247,0.0006407141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000569388,"about_ca_system_score_gemma":0.0005800994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007261183,"about_ca_topic_score_gemma":0.01030868,"domain_scores_codex":[0.9982603,0.0007823975,0.0001100862,0.0003994863,0.0002452075,0.0002024877],"domain_scores_gemma":[0.9906203,0.006393601,0.0009053839,0.0008628397,0.0008948265,0.000323119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006859855,0.003484689,0.8171238,0.0003011606,0.0004064764,0.0006546169,0.002885351,0.01023049,0.004575547,0.001268607,0.007819168,0.1505642],"study_design_scores_gemma":[0.00004587342,0.0006474785,0.4791508,0.0000887861,0.0001953044,0.0006040242,0.004494172,0.5038053,0.003118315,0.002374241,0.005397368,0.00007844221],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846942,0.0001961475,0.01109324,0.0004601858,0.00007175966,0.0002103846,0.001699342,0.0001317164,0.001443041],"genre_scores_gemma":[0.9798762,0.0001192793,0.01496244,0.0001546174,0.00009851315,0.0001634863,0.003296712,0.00002458272,0.001304187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007261183,"threshold_uncertainty_score":0.01542175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03485767340195346,"score_gpt":0.3390584428319096,"score_spread":0.3042007694299562,"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."}}