Effect of variations in treatment regimen and liver cirrhosis on exposure to benzodiazepines during treatment of alcohol withdrawal syndrome
Bibliographic record
Abstract
PURPOSE: Benzodiazepines (BDZs) are the drugs of choice to prevent the symptoms of alcohol withdrawal syndrome (AWS). Various treatment protocols are published and have been shown to be effective in both office-managed and facility-managed treatment of AWS. The aim of this scientific commentary is to demonstrate the differences in the expected exposure to BDZs during AWS treatment using different treatment regimens available in the literature, in patients with or without alcoholic liver cirrhosis. METHODS: Diazepam and lorazepam AWS protocols were examined and reviewed in the literature, and blood plasma levels were examined and compared, respectively. RESULTS: Considerable variation in the blood levels with the different dosing schedules was found. Because the drugs are metabolized differently, we have also shown that liver disease affects the blood levels of diazepam, but not of lorazepam. CONCLUSIONS: Differences in treatment regimens, the choice of BDZ, as well as the presence of liver cirrhosis can substantially alter the exposure of patients to drugs used for AWS treatment. Outpatient treatment of AWS has been shown to be relatively safe and effective for the treatment of AWS but patients should be carefully monitored.
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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 | 0.003 | 0.036 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".