Applying a Modified Version of the Prediction of Alcohol Withdrawal Severity Scale in a Canadian Community Withdrawal Management Setting
Bibliographic record
Abstract
INTRODUCTION: Severe alcohol withdrawal syndrome (SAWS) can lead to significant morbidity and mortality. The Prediction of Alcohol Withdrawal Severity Scale (PAWSS) has been validated in general acute care environments, but its efficacy in withdrawal management settings remains underexplored. This study aimed to assess the utility of a modified PAWSS and identify appropriate cutoff scores in a community withdrawal management setting in Vancouver, Canada. METHODS: From October 2019 to September 2022, we reviewed charts at Vancouver Detox Centre. Modified PAWSS versions replaced question 9 on the original PAWSS with: (i) breath analysis readings; (ii) alcohol consumption in the previous 24 h; and (iii) clinical assessments. We performed receiver operating characteristic analysis and used Youden's index to determine modified PAWSS' diagnostic accuracy against SAWS presentation, defined by a score of 15 or greater on the Clinical Institute Withdrawal Assessment Alcohol, Revised, seizures or delirium tremens and/or benzodiazepine administration. RESULTS: Among 228 individuals (165 male, 63 female), 175 (75%) met SAWS criteria during admission. For breath analysis readings, an optimal PAWSS cutoff score had 55% sensitivity (95% confidence interval [CI] 46%-63%) and 74% specificity (95% CI 54%-87%). For alcohol consumption in the last 24 h, a cutoff score of 7 had 44% sensitivity (95% CI 36%-51%) and 85% specificity (95% CI 70%-93%). For clinical assessment, a cutoff score of 6 had 53% sensitivity (95% CI 45%-61%) and 71% specificity (95% CI 58%-85%). DISCUSSION AND CONCLUSIONS: Within a community withdrawal setting, the prevalence of SAWS was high, rendering the modified PAWSS less valuable. Although higher cutoff scores improved specificity, poor sensitivity hindered identification of low-risk SAWS individuals.
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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 | 0.000 | 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 | 0.000 |
| 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".