Exercise based multisite pain management in temporomandibular dysfunction and chronic low back pain: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Temporomandibular disorders (TMDs) and chronic low back pain (CLBP) frequently coexist, with CLBP reported in 29% of TMDs cases. This co-occurrence emphasizes the importance of multisite pain management approaches. OBJECTIVE: To compare the effects of Rocabado's exercises alone or combined with structured back exercises on TMD-related pain, mandibular range of motion (ROM), and CLBP severity. METHODS: Forty participants with myogenic TMD and nonspecific CLBP were randomly assigned to a control group (CG, Rocabado only) or complex exercise group (CEG, Rocabado + back exercises). Both underwent 24 supervised sessions over six weeks. Pain was assessed via the Graded Chronic Pain Scale (GCPS-2.0), Short Form-McGill Pain Questionnaire (SF-MPQ), and Numeric Pain Rating Scale (NPRS). Jaw function and ROM were evaluated using the Jaw Functional Limitation Scale (JFLS-20) and objective mandibular movement measures. Oral health-related quality of life was assessed via the Oral Health Impact Profile-14 (OHIP-14). RESULTS: < .001, d > 0.8). CONCLUSION: Rocabado's exercises reduced TMDs pain and improved jaw function, while structured back exercises provided additional benefits for the pain severity of CLBP and sensory pain of TMDs. TRIAL REGISTRATION: Clinical trial approval was obtained at https://www.clinicaltrials.gov/, and the registration status was made publicly available with the number NCT06343155 on April 1, 2024. (https://clinicaltrials.gov/study/NCT06343155).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".