The Use of Anticonvulsant Adjuncts to Treat Alcohol Withdrawal Syndrome in Older Adults
Bibliographic record
Abstract
Background: We evaluated the prescribing practices of anticonvulsant (AC) adjuncts to benzodiazepines in managing Alcohol Withdrawal Syndrome (AWS). We also examined the prescription of relapse prevention agents for Alcohol Use Disorder (AUD), and adverse events related to AWS treatment. Methods: Records were reviewed retrospectively pertaining to medically ill adults aged 60 and above with AWS and admitted to a medicine or hospitalist unit of a Canadian centre between June 2013 and June 2018. Duration and dosing of benzodiazepine, dosing and type of AC and AUD agent, and adverse events were collected. A multivariable regression model was employed. Results: 83 encounters were included in the study and 28 were prescribed an AC. The amount and duration of benzodiazepine administered were not statistically different between the benzodiazepine only and the AC adjunct groups, once severe AWS complications were accounted for. Five new prescriptions of traditional AUD agents were provided on discharge. No AC-associated adverse events occurred. Conclusions: AC adjuncts for AWS did not decrease the amount of benzodiazepine administered nor shorten the duration of treatment. Their routine use is not supported by our findings. Our study highlights a missed opportunity for AUD agents to be prescribed during hospitalization.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".