Qualitative and quantitative assessment of headaches in people with temporomandibular joint disorders: A pilot study
Bibliographic record
Abstract
BACKGROUND: Headaches (HAs) and temporomandibular joint dysfunction (TMD) are common comorbidities, and the presence of one of them in a patient increases the incidence of the other. The relationship between these 2 conditions may involve common pathophysiological processes. Considering the topicality of the problem, it is justified to conduct research in this field. In this study, we assessed HA type and severity in people with TMD. OBJECTIVES: The aim of the study was to conduct qualitative and quantitative assessments of HAs in people with temporomandibular joint (TMJ) disorders. MATERIAL AND METHODS: The study group consisted of 51 subjects of both sexes with a TMD diagnosed using the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) test. A self-report questionnaire was utilized to self-assess the presence of TMD symptoms, while the standardized Short-Form of the McGill Pain Questionnaire was used to qualitatively and quantitatively assess HAs. RESULTS: People with TMD were significantly more likely to report HA occurrences (p < 0.001). Pain intensity was statistically significantly higher among individuals with TMD compared to those without TMD symptoms (p < 0.001). Most often, the HA was associated with a pressing pain (r = 0.82) and least often, it was described as cutting (r = 0.30). Neck and shoulder girdle pain (p = 0.059; 82.9%) and clenching and/or grinding of teeth (p = 0.021; 92.7%) were significantly more common among patients who declared HAs than among those without HAs. The results obtained so far may indicate a significant relationship between HA and TMD. CONCLUSION: We have described the relationship between the occurrence of HAs and TMD. Headaches are more frequent and more severe in people with TMD.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".