A Retrospective Study of the Adjunctive Use of Gabapentin With Benzodiazepines for the Treatment of Benzodiazepine Withdrawal
Bibliographic record
Abstract
Benzodiazepine withdrawal is a widespread problem with potentially severe and deadly consequences. Currently, the only medications available for treating benzodiazepine withdrawal are short-acting and long-acting benzodiazepines. Identifying other drugs to help in treating benzodiazepine withdrawal is necessary. Gabapentin, an anxiolytic drug that is also used off-label to treat alcohol withdrawal, is a potential candidate for modulating benzodiazepine withdrawal. Using electronic records from a large inpatient psychiatric facility, a retrospective study of 172 patients presenting with benzodiazepine withdrawal was conducted to determine if the coincidental use of gabapentin for other medical conditions was associated with better outcomes of benzodiazepine withdrawal (N=57 gabapentin, N=115 no gabapentin). The primary outcomes were hospital length of stay and total amount of benzodiazepines given (lorazepam milligram equivalent). In this retrospective analysis of electronic medical record data, the patients experiencing benzodiazepine withdrawal who received gabapentin as an adjunct to the use of benzodiazepines were administered a smaller amount of benzodiazepines and had a shorter length of hospital stay relative to the comparison group who did not receive adjunctive gabapentin. These results suggest the potential use of gabapentin as an adjunct to the use of benzodiazepines for treating benzodiazepine withdrawal. The limitations of this study included a small sample size and variability in medication management strategies across the sample.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".