Enhancing Physician Management of Nontraumatic Dental Conditions: Impact of an Online Educational Module
Bibliographic record
Abstract
BACKGROUND: Dental emergencies from nontraumatic dental conditions (NTDCs) cause tooth infections and pain. These cases are often seen by physicians in nondental settings. Definitive treatment is rarely possible in these settings. This often leads to prescribing antibiotics or opioid analgesics, which may conflict with stewardship principles. OBJECTIVES: This study evaluated the impact of an online educational module on physicians' confidence and knowledge in managing NTDCs. METHODS: An educational module combining a cognitive aid and a multimodal online module with theoretical clinical cases was developed for medical students, licensed family physicians, and emergency physicians. It focused on evidence-based management of NTDCs in nondental settings. The impact was measured through pre- and postmodule self-evaluations and clinical case tests. Analyses were conducted using descriptive statistics and t-tests (p < 0.05). RESULTS: A total of 39 participants completed all aspects of the intervention. Improvements in confidence and knowledge were observed, including better accuracy in emergency management, antibiotic prescription, and local anesthesia administration. There was a reduction in opioid prescriptions and antibiotic overprescriptions, with an increased tendency to delay prescriptions in favor of definitive treatment. Course evaluations showed strong agreement on the quality and usefulness of the educational module. CONCLUSIONS: The online educational module effectively enhanced physician confidence and knowledge in managing tooth infections and pain. Its implementation may help address global concerns about antibiotic overuse and resistance, while also promoting a more responsible approach to the opioid crisis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".