What to Do and Not Do in Infection Control
Bibliographic record
Abstract
The Royal College of Dental Surgeons of Ontario introduced a new dental opioid prescribing guideline in November 2015. The authors examined whether introduction of this guideline was associated with changes in opioid prescribing patterns.The authors conducted a population-based, cross-sectional time series study of Ontarians who received opioids prescribed by dentists from July 1, 2012 through September 30, 2017. They examined the impact of the guideline on dental prescribing patterns by calculating the monthly rate of opioid dispensing from dentists per 100,000 population, as well as the population exposure to opioids expressed as milligram morphine equivalents per 100 population.Ontario dentists issued 1,571,897 opioid prescriptions to 1,157,102 patients over the study period. The guideline was not associated with a change in opioid dispensing rates, but it was associated with a significant reduction in the volume of opioids dispensed (28.1% reduction, from 22.1 to 15.9 milligram morphine equivalents per 100 population from October 2015 through September 2017; P = .01).Introduction of the prescribing guideline was associated with no change in the rate of opioid prescribing by dentists, but it was associated with a roughly 25% reduction in the volume of opioids prescribed.Introduction of the new opioid prescribing guideline for Ontario dentists was associated with a reduction in the overall volume of opioids dispensed.
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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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".