Law enforcement‐derived data on gabapentin diversion and misuse, 2002‐2015: diversion rates and qualitative research findings
Bibliographic record
Abstract
PURPOSE: Recent limited epidemiologic and case reports suggest that gabapentin is being misused, especially among prescription opioid misusers. However, no apparent studies have reported data from law enforcement on the diversion and misuse of gabapentin. METHODS: Case report data are drawn from a quarterly survey of prescription drug diversion completed by a national sample of law enforcement and regulatory agencies who engage in drug diversion investigations. Rates of gabapentin diversion per 100 000 population were calculated for each quarter from 2002 through 2015. Qualitative data are drawn from a brief questionnaire completed by a subsample of survey respondents and were organized and presented by theme. RESULTS: In total, 407 new cases of diverted gabapentin were reported during the time period, with diversion rates steadily increasing from zero cases in the first 2 quarters of 2002 to a high of 0.027 cases per 100 000 population in the fourth quarter of 2015. Qualitative data suggest that gabapentin is being misused in conjunction with prescription opioids and that gabapentin and heroin are being combined and consumed together. Law enforcement reporters found these drug use trends to be contributing to gabapentin diversion. CONCLUSIONS: The recent increase in gabapentin diversion appears to be related to the opioid epidemic, based on law enforcement descriptions of gabapentin being misused in combination with opioids. Yet epidemiological data related to this finding is limited and research conducted among gabapentin misusers is needed to understand this problem in more depth. Greater monitoring of gabapentin abuse and diversion appear warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".