{"id":"W4415885991","doi":"10.2196/77279","title":"Monitoring Opioid-Related Social Media Chatter Using Natural Language Processing and Large Language Models: Temporal Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Mental Health via Writing","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Social media; Natural language; Opioid overdose; Natural (archaeology); Language identification; Poison control; Action (physics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002845455,0.001291321,0.000567689,0.003748616,0.0006813571,0.001169282,0.001001201,0.0008679467,0.001351258],"category_scores_gemma":[0.009683358,0.0003546819,0.001395512,0.001803761,0.0005825282,0.002004031,0.001376358,0.001893429,0.001517704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145088,"about_ca_system_score_gemma":0.001123322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01356449,"about_ca_topic_score_gemma":0.02379483,"domain_scores_codex":[0.9974167,0.0009932936,0.0002581817,0.0007517874,0.0004344776,0.0001455632],"domain_scores_gemma":[0.990086,0.00718023,0.001020832,0.0005564662,0.0009267837,0.0002296521],"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":[0.001560282,0.002187314,0.2111297,0.002738432,0.0007220303,0.003334889,0.005042387,0.072179,0.06894486,0.00663761,0.05528628,0.5702372],"study_design_scores_gemma":[0.00005914739,0.0002361676,0.06556115,0.0001059729,0.0001187224,0.0005726442,0.001197058,0.9048858,0.008956715,0.005314587,0.01288335,0.0001086893],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6962096,0.002735644,0.2231492,0.003660815,0.0005187665,0.001299676,0.05499985,0.01064429,0.0067821],"genre_scores_gemma":[0.7717228,0.0005793273,0.1745304,0.0004797388,0.0003186602,0.00106062,0.04809557,0.0002752424,0.002937608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01356449,"threshold_uncertainty_score":0.0269711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04319238565082803,"score_gpt":0.4264366241931189,"score_spread":0.3832442385422908,"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."}}