A Retrospective Study of the Adjunctive Use of Gabapentin With Benzodiazepines for the Treatment of Benzodiazepine Withdrawal
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it