Bibliographic record
Abstract
OBJECTIVE: To describe patterns of antibiotic purchasing by Canadian hospitals from five regions in Canada between 2007 and 2011. METHODS: The Canadian Drugstore and Hospital Purchases Audit (CDH) dataset measures the dollar value and unit volume of pharmaceutical products from over 700 hospitals and is extrapolated to represent purchases by more than 800 hospitals in Canada. General population information was used for developing the rates of purchasing with a primary focus on unit volume. RESULTS: In 2011, there was a 7% increase in total antibiotics purchased by Canadian hospitals compared to 2010, with slight increases in the purchasing levels for oral (8% increase) and parenteral (3% increase) antibiotics. Antibiotics considered of very high importance to human medicine (Category I) continued to represent a high proportion of the total antibiotics used in hospitals in 2011. Overall, consumption of antibiotics was highest in Manitoba at 2.61 doses per 1,000 inhabitants per day (DID), while Ontario had the lowest levels of consumption (1.26 DID). New Brunswick had the highest proportion of Category I consumption (43%, 0.62/1.43 DID) for 2011, driven by higher levels of fluoroquinolones consumed in that province. CONCLUSION: Canadian hospitals have purchased an increasing number of antibiotics and are consuming slightly more oral and parenteral antibiotics. Overall, consumption was highest in Manitoba and total cost was highest in British Columbia. Ontario had the lowest level of consumption of anitbiotics and the lowest overall cost.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".