The state of dentists’ knowledge about the relationship between temporomandibular disorders and Lyme disease symptoms
Bibliographic record
Abstract
Introduction: Dentists may be the first to whom the patient with Lyme disease (LD) reports symptoms of temporomandibular disorders (TMD).For this reason, it is important that dentists should be aware of the relationship between LD and TMD.Objectives: Aim of this study was to assess dentists' knowledge about the relationship between LD and TMD.Material and methods: A group of 201 dentists participating in the project were volunteers, who came from randomly selected dental offices.All the participants anonymously completed a questionnaire, in which they were asked three questions concerning TMD and LD.Results: Among dentists, 81.1% agreed that LD symptoms could mimic TMD, and 81.1% of the respondents thought that fluctuating pain in the area of temporomandibular joint and/or myofascial muscles imitate TMD.Among participants, 37.8% indicated limited mouth opening, whereas 19.4% of respondents marked sounds as symptoms.There was a significant relationship (p < 0.05) between those dentists who knew that LD symptoms can mimic TMD, as they considered the possibility of Lyme disease more often than others. Conclusions:The results of this study indicate that dentists' knowledge of tick and tick-borne diseases is sufficient, and it would be a relevant public health strategy as it could reduce the risk of unnecessary TMD treatment for patients suffering from LD.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".