What Accounts for a Large Increase in Antibiotic Prescribing by Dentists?
Bibliographic record
Abstract
Background. While community antibiotic use by physicians has been declining in British Columbia (BC), we launched this study to begin to explain a troubling trend toward increased prescribing by dentists. Methods. All outpatient prescriptions are entered in the BC Pharmanet database. We obtained anonymized, line-listed data on prescriptions from 1996 to 2013, including a variable coding for licensing body of the prescriber. Analyses were conducted in SAS and Excel using Anatomical Therapeutic Classification (ATC) standard codes and defined daily dose values. Rates of prescribing and utilization were normalized to the BC population and expressed in defined daily doses per 1000 persons per day (DID). To evaluate the reasons for trends, a webinar was held through the Canadian Dental Association, inviting correspondence from dentists about the drivers of antibiotic use in practice. Results. From 1996 to 2013, overall community antibiotic use in BC declined from 18.2 to 15.9 DID and physician prescribing for antibiotics declined 18.2% from 17.4 to 14.1 DID. However, dental surgeons increased their rate 62.2% from 0.98 to 1.59 DID and their proportionate contribution of prescriptions from 6.7% to 11.3%. The rate of prescribing increased the most for dental patients aged 60 and over. We had 30 communications from US and Canadian dentists in response to the webinar and the following explanatory themes emerged: unnecessary prescription for periapical abscess and irreversible pulpitis; increase in use of dental implants and associated complications; slow adoption of newer guidelines calling for less perioperative antibiotic coverage for patients with valvular heart disease and prosthetic joints; emphasis on cosmetic practice reducing the surgical skill-set of the average dentist; under-insurance driving antibiotics as a substitute for surgery; aging population; and more dental registrants per capita. Conclusion. The above themes should be further validated in other studies but may already guide priorities in antibiotic stewardship for continuing dental education. Disclosures. F. Marra, Merck Canada Inc: Grant Investigator, Research grant
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".