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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".