Evaluation of a Symptom‐Triggered Protocol Approach to the Management of Alcohol Withdrawal Syndrome in Older Adults
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether implementation of symptom-triggered administration of a benzodiazepine protocol reduces the severity (total cumulative dose), duration, and complications of alcohol withdrawal syndrome (AWS). DESIGN: Retrospective health record review. SETTING: Tertiary care center in Vancouver, Canada. PARTICIPANTS: Individuals aged 70 and older admitted to the Acute Care for Elders and Acute Medicine Unit wards with diagnostic codes for AWS from 2008 to 2012. MEASUREMENTS: Median duration and cumulative dose of benzodiazepine treatment, number of severe AWS complications, severe benzodiazepine-associated adverse effects, and need for adjunct therapy. RESULTS: Thirty-three participants in the preprotocol group and 30 in the protocol-implemented group met the inclusion criteria. Median duration of benzodiazepine treatment decreased from 96 hours (interquartile range (IQR) 72-120 hours) in the preprotocol period to 48 hours (IQR 0-108 hours; P=.04), and median cumulative benzodiazepine dose administered decreased from 9 mg (IQR 5-19.8 mg) to 3 mg (IQR 0-10 mg; P=.001). Statistically significantly lower incidence of severe AWS complications (P=.007) and adjunct therapy use (P=.02) was seen in the protocol-implemented group. CONCLUSION: A symptom-triggered protocol for dosing of benzodiazepine therapy in the management of AWS in individuals aged 70 and older significantly reduced the total duration of benzodiazepine use, cumulative benzodiazepine dose, and use of adjunctive medications in the treatment of AWS.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".