{"id":"W4415055368","doi":"10.31083/ijp44192","title":"Understanding the Opioid Overdose Crisis: A Comprehensive Bibliometric Analysis","year":2025,"lang":"en","type":"article","venue":"International Journal of Pharmacology","topic":"Opioid Use Disorder Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scopus; Opioid overdose; Thematic analysis; Citation; Drug overdose; Medical prescription; Bibliometrics; Citation analysis; Opioid","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.00929077,0.0006850055,0.001895905,0.1875472,0.001732889,0.008460155,0.000905154,0.001050822,0.003222282],"category_scores_gemma":[0.04274121,0.0003727128,0.001716905,0.2251432,0.001030138,0.007772129,0.003903798,0.0006193243,0.0005515327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003817742,"about_ca_system_score_gemma":0.006407724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007494454,"about_ca_topic_score_gemma":0.00667389,"domain_scores_codex":[0.9918869,0.001869484,0.001750014,0.0007256473,0.003288972,0.0004789487],"domain_scores_gemma":[0.9537174,0.03052635,0.006750586,0.001275044,0.00685263,0.000878012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002036644,0.0002011456,0.4688761,0.03014205,0.002073853,0.001438532,0.01457476,0.004473449,0.001693949,0.02034583,0.02755442,0.4284223],"study_design_scores_gemma":[0.00003924703,0.0002215304,0.7547323,0.01376275,0.002482017,0.001888507,0.05123833,0.01208021,0.001327906,0.01928567,0.1427083,0.0002332342],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7473776,0.1171181,0.009752642,0.01453025,0.0002845806,0.001210736,0.06087517,0.000473728,0.04837724],"genre_scores_gemma":[0.8979201,0.06357731,0.01292705,0.0003508666,0.0003588944,0.0007075442,0.02272873,0.00006289347,0.001366584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8124528,"threshold_uncertainty_score":0.04913485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0628334564196168,"score_gpt":0.4044182605392684,"score_spread":0.3415848041196516,"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."}}