Shoulder Girdle Disability, Dysfunction, and Pain in Participants With Temporomandibular Joint Disorders: A Cross-Sectional Survey on Prevalence and Associations
Bibliographic record
Abstract
Purpose: There is a rising prevalence of temporomandibular disorders (TMD) and, therefore a need to improve the management of these conditions. No studies have been done to assess the possible associations between the shoulder girdle and the temporomandibular joint (TMJ) in participants with TMD. The aim of the study was to estimate the prevalence of disability, dysfunction and pain in the shoulder girdle in participants with TMD. In addition, the association between the disability, dysfunction and pain in the shoulder girdle and TMJ in participants with temporomandibular disorders was investigated. Method: A cross-sectional study was performed where participants were invited to complete an online survey if they received a score of two or more on the TMD-pain screener. The survey included questions on demographics, the TMJ, and the shoulder girdle. Results: A total of 186 individuals with TMD participated in the study of whom, 56% ( n = 104) reported having shoulder pain and 45% ( n = 84) reported having previous shoulder treatment. A positive correlation was found between the level of TMD, as indicated by the mean Fonseca Anamnestic index score and the pain ( r = 0.29; p < 0.001), disability ( r = 0.24; p = 0.001), and total scores ( r = 0.28; p < 0.001) on the Shoulder Pain and Disability index and similarly with shoulder pain at its worst ( r = 0.19; p = 0.009). Conclusions: The associations found between the pain and disability of the shoulder girdle and TMJ give insight into the relationship between the two areas in participants with TMD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".