SYMPTOMS‐TRIGGERED APPROACH VERSUS FIXED‐SCHEDULED APPROACH OF BENZODIAZEPINES FOR MANAGEMENT OF ALCOHOL WITHDRAWAL SYNDROME: NON‐RANDOMIZED CONTROLLED TRIAL
Bibliographic record
Abstract
Background: Benzodiazepines are the drugs of choice for the treatment of Alcohol withdrawal syndrome (AWS) and the regimen can be as fixed-scheduled or based on symptoms-triggered approach. Due to limited information, we aimed to assess the effect of symptoms-triggered approach using (CIWA-Ar) score compared to fixed-scheduled of benzodiazepines for treatment of AWS on total dose, length of hospital stays (LOS), and rate of 90-days readmissions. Methodology: A single-center prospective non-randomized controlled group included all patients diagnosed with AWS and treated with benzodiazepines. Those admitted between 1st October 2019 to 30th September 2020 were treated with fixed-scheduled regimen (n=150) while all patients admitted between 1st November 2020 to 31st October 2021 were treated with symptoms-trigger regimen (n=50). Result: Symptoms-triggered regimen group showed a significant lower 90-days readmissions rate (HR 2.61,95% CI =1.18-6.84 p=0.01). Kaplan–Meier survival analysis showed a significantly shorter duration to the first 90-days readmission in the fixed-scheduled regimen group (HR 2.3,95% CI 5.6-1.16; p=0.02). Symptoms-triggered regimen group required a significantly lower dose of diazepam (40 mg vs 10 mg; p<0.01) and a higher dose of thiamine (800 mg vs 600 mg; p<0.01). LOS was significantly increased in symptoms-triggered regimen group (3.9 vs 2.2 (IQR, days); p<0.01). Conclusion: The use of a symptoms-triggered regimen for treatment of AWS was associated with reduction of 90-days readmissions, increased duration to the first readmission, and reduced total dose of benzodiazepines, but longer LOS. Overall, symptoms-triggered regimen for treatment of AWS is safe, cost-effective and associated with reduced alcohol dependence relapse. Keywords: Alcohol withdrawal syndrome; benzodiazepines; fixed-scheduled regimen; symptoms-triggered regimen; CIWA-Ar score.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".