Temporomandibular disorder and headache prevalence: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Temporomandibular disorders (TMD) and headaches are prevalent among the global population. Patients often suffer from both conditions, and they are likely to be associated in a bidirectional way. However, the nature of the association remains unclear. Understanding the epidemiological aspects of the relationship between these conditions could have important clinical implications. Objective: To evaluate the prevalence of headaches in TMD patients as well as the prevalence of TMD in patients who suffer from headaches. Method: A systematic literature search was conducted using electronic databases. Studies published in English and those that used an acknowledged diagnostic criteria for TMD and headaches were included. Study quality was assessed using the Newcastle-Ottawa scale and meta-analyses were performed to generate pooled prevalence estimates. Result: Thirty-one studies met the selection criteria for the review; 16 studies evaluated the prevalence of headache in TMD patients and 15 studies evaluated the prevalence of TMD in headache patients. The included studies were of moderate-to-high quality. Meta-analyses revealed moderate-to-large heterogeneities across included studies. Pooled prevalence estimates from meta-analyses indicated similar rates of headaches in TMD patients and of TMD in headache patients (61.58%, 95% CI 45.26–76.66 and 59.42%, 95% CI 51.93–66.60, respectively). Migraines were more commonly observed in TMD patients (40.25%, 95% CI 35.37–45.23) compared to tension-type headaches (18.89%, 95% CI 12.36–26.44). The prevalence of headaches was particularly high in painful-TMD (82.80%, 95% CI 75.41–89.10). Conclusion: Despite large variance in prevalence rates across included studies, this review suggests headache and TMD frequently co-occur, particularly in the case of migraines and muscle related TMD. This association has important clinical, pathophysiological and therapeutic implications.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".