{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007383836,0.0002599162,0.0006243172,0.000501842,0.0003288625,0.00003181012,0.0002098381,0.0004528268,0.0001457972],"category_scores_gemma":[0.00006951856,0.0002566821,0.0001243484,0.0007507893,0.0001341589,0.0001882858,0.0002412571,0.0007126504,0.000009979143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001535966,"about_ca_system_score_gemma":0.00005868455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004557935,"about_ca_topic_score_gemma":0.0001416231,"domain_scores_codex":[0.9975475,0.0003523477,0.0006973046,0.0005234344,0.0001285235,0.0007509031],"domain_scores_gemma":[0.9990327,0.0002983339,0.0002848862,0.0002374917,0.00005219972,0.00009435618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001746012,0.0001366942,0.7935335,0.000329164,0.0006112986,0.000113275,0.1420814,0.0000947279,0.00100559,0.002328651,0.0002764658,0.05931466],"study_design_scores_gemma":[0.004628445,0.00006100437,0.662465,0.0004110546,0.0008449795,0.00006979013,0.0982051,0.2296815,0.0001744085,0.002142593,0.0002045465,0.001111627],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867535,0.008715753,0.0009219258,0.0002511331,0.0007064481,0.0003107087,0.00002251389,0.0001727709,0.002145299],"genre_scores_gemma":[0.9973779,0.000006409756,0.001096869,0.0007579508,0.0002961601,0.00008598638,0.00006144187,0.00002348539,0.0002937392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2295868,"threshold_uncertainty_score":0.9999886,"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."}}