Orofacial pain education in dentistry: A path to improving patient care and reducing the population burden of chronic pain
Bibliographic record
Abstract
Dentists stand in an optimal position to prevent and manage patients suffering from chronic orofacial pain (OFP) disorders, such as temporomandibular disorders, burning mouth syndrome, trigeminal neuralgia, persistent idiopathic dentoalveolar pain, among others. However, there are consistent reports highlighting a lack of knowledge and confidence in diagnosing and treating OFP among dental students, recent graduates, and trained dentists, which leads to misdiagnosis, unnecessary costs, delay in appropriate care and possible harm to patients. Education in OFP is necessary to improve the quality of general dental care and reduce individual and societal burden of chronic pain through prevention and improved quality of life for OFP patients. Our aims are to emphasize the goals of OFP education, to identify barriers for its implementation, and to suggest possible avenues to improve OFP education in general, postgraduate, and continuing dental education levels, including proposed minimum OFP competencies for all dentists. Moreover, patient perspectives are also incorporated, including a testimony from a person with OFP. General dentists, OFP experts, educators, researchers, patients, and policy makers need to combine efforts in order to successfully address the urgent need for quality OFP education.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".