Gabapentin abuse: A case presentation on how to manage this growing concern
Bibliographic record
Abstract
Gabapentin was first approved by the US Food and Drug Administration in 1993 as an adjunct treatment of epilepsy. In 2004, an additional indication of pain associated with post-herpetic neuralgia was added. Misuse of gabapentinoids dates back to 2010 while surging recently to the tenth most commonly prescribed medication in 2016. Abuse can be as high as 65% for even those who legally obtained the medication through a prescription. It is used off-label up to 95% of the time despite limited evidence of its efficacy particularly with multiple pain types. The surge in misuse can be attributed not only to off-label use but also an assumption of no abuse potential coupled with clinicians seeking alternative treatment options to the opioids. More common side-effects include sedation, dizziness, and cognitive difficulties. However, even normal dosing can produce side-effects similar to other addictive substances including: euphoria, talkativeness, and increased energy (opioids); sedation (opioids, benzodiazepines); and dissociation (hallucinogens). In fact, a few states including Kentucky, Ohio, and West Virginia will or have already added gabapentin to the controlled substance rosters even though no federal designation is in place. Identified risks for gabapentin misuse in the literature are limited with the exception of a history of or current substance abuse, particularly opioids. Unfortunately, gabapentin is often co-prescribed with opioids lending to a heightened risk of opioid-related mortality. Clinicians must understand that gabapentin is not effective for a variety of pain conditions nor is a routine substitute for opioids. In addition, close monitoring practices often associated with opioids and benzodiazepines (i.e., regular monitoring for aberrant drug taking behaviors, limits on supply, guarded dose titration) should be applied to that of gabapentin.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".