Antidote availability in Quebec hospital pharmacies: impact of N-acetylcysteine and naloxone consumption.
Bibliographic record
Abstract
OBJECTIVES: To study the availability of 13 specific antidotes in hospitals and correlate the availability of those antidotes to the number of poisonings seen in hospitals using N-acetylcysteine and naloxone consumption as a surrogate. METHODS: Pharmacy directors of hospitals with an emergency department were surveyed for number of adequately stocked antidotes (N-acetylcysteine, ethanol, cyanide antidote kit or hydroxycobalamine, deferoxamine, digoxin-immune FAB, dimercaprol, flumazenil, glucagon, methylene blue, naloxone, physostigmine, pralidoxime and pyridoxine). RESULTS: Data were obtained from 96 of 112 (86%) of the pharmacies surveyed. Number of adequately stocked antidotes per hospital ranged from zero to nine of 13. There was a correlation between all hospital characteristics evaluated and the number of adequately stocked antidotes (P<0.05). Correlations between the number of adequately stocked antidotes and the amount of N-acetylcysteine and naloxone consumed were significant (rs=0.58, P<0.001; r(s)=0.53, P<0.001). The amount of N-acetylcysteine consumed, the number of annual visits to the emergency department and the number of hours of pharmacy coverage on weekends independently predicted the presence of adequately stocked antidotes. CONCLUSIONS: Larger hospitals are more likely to have adequate stocks of antidotes. Adequate stocking of antidotes is significantly correlated with the amount of N-acetyl- cysteine and naloxone consumed. This suggests that hospitals more likely to see serious acetaminophen and opiate poisonings are more likely to maintain adequate stocks of antidotes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".