Bibliographic record
Abstract
The cost of maintaining adequate antidote supplies David Juurlink and colleagues re-ported recently that most acute care hospitals in Ontario do not stock adequate amounts of antidotes.1 We previously showed that the availability of 13 antidotes was also poor in Quebec (we used more stringent criteria to de-fine adequate stocking).2 Although the situation is worrying, it is probably not as expensive to cor-rect as it may seem. The antidote in-ventory would only have to be in-creased by 6 to 18 % to correct the problem in Quebec,3 because there is gross overstocking of some antidotes by some hospitals. Because we set our rec-ommended minimal stock of 18 anti-dotes on the basis of levels of hospital care, we think that keeping an adequate antidote inventory should not be a problem even for smaller hospitals with limited pharmacy budgets; the annual costs in 2000 would have been $4697 for primary care hospitals, $7450 for secondary care hospitals and $14 273 for tertiary care hospitals. Our recom-mended minimal amount of stock was that which would provide an adequate amount of antidote to treat a 70-kg adult for 12 hours in a primary or sec-ondary care hospital and 24 hours in a tertiary care hospital.3 Most antidotes are used infrequently: the turnover of antidote inventory is 0.3 to 7.4 per year compared with an average of 8.9 per year for all medications in Canadian pharmacies.3 If a hospital uses antidotes appropriately the cost of maintaining an adequate stock should not be a con-cern, considering that most antidotes can be credited if unused.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.457 | 0.262 |
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".