Antibiotic and Opioid Analgesic Prescribing Patterns of Dentists in Vancouver and Endodontic Specialists in British Columbia.
Bibliographic record
Abstract
AIM: To assess the prescribing decisions of general dentists in Vancouver and endodontists in British Columbia regarding analgesics and antibiotics for patients with endodontic disease. An additional aim was to determine whether gender, clinical experience or practice location have an impact on such decisions. METHODS: A 4-page survey was developed and distributed to approximately half the general dentists in Vancouver (n = 259) and all of the endodontists in British Columbia (n = 50). Basic demographic questions were followed by 7 clinical scenarios and a list of endodontic diagnoses. Participants were asked questions regarding their decisions to prescribe analgesics and antibiotics. RESULTS: The overall response rate was 49%: 72% of endodontists responded, compared with 45% of general dentists. A substantial proportion of clinicians prescribed opioid analgesics and antibiotics in the various clinical scenarios. The rate of prescription of opioid analgesics ranged from 4%-47% for the various scenarios; the rate of prescription of antibiotics was 5%-88%. General dentists reported prescribing opioid analgesics and antibiotics more often than endodontists. Gender, clinical experience and practice location did not seem to have an impact on the decision to prescribe opioid analgesics or antibiotics. CONCLUSIONS: Based on the results of our survey, a significant proportion of dental professionals in British Columbia prescribe opioid analgesics and antibiotics during endodontic procedures when they are likely not required. General dentists tend to prescribe these medications more often than endodontists. We found no apparent differences in terms of gender, clinical experience or practice location. Dentists must be conscientious when prescribing these medications as over-prescription has negative consequences.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".