Back and neck pain: A comparison between acute and chronic pain–related Temporomandibular Disorders
Bibliographic record
Abstract
Background: Temporomandibular disorders (TMDs) are common and cause persistent pain. Comorbidities are associated with TMDs and can affect the effectiveness of their treatments. The literature is lacking enough evidence on the difference between acute and chronic pain, particularly in TMDs. Investigating this difference could highlight potential risk factors for the transition from acute to chronic pain-related TMDs. Aim: To compare the likelihood of back and neck pain (BP, NP) between acute and chronic pain-related TMDs (AP-TMD, CP-TMD) as defined by pain duration and pain-related disability.. Methods: Participants with AP-TMDs (≤3 months) and CP-TMDs (>3 months) were recruited according to the diagnostic criteria and research diagnostic criteria of TMD. BP and NP were assessed using a self-reported checklist. CP-TMDs defined by disability (chronic disability) and depression and anxiety symptoms were assessed using validated instruments. Logistic regression analyses were employed. Results: = 369). Relative to AP-TMD, participants with CP-TMD had twice the odds of reporting NP (odds ratio [OR] = 2.17, 95% CI 1.27-3.71) but not BP (OR = 0.96, 95% CI 0.57-1.64). Participants with chronic disability were twice as likely to report NP (OR = 1.95, 95% CI 1.20-3.17) but not BP (OR = 1.13, 95% CI 0.69-1.82) compared to those without. All analyses were adjusted for age, sex, and anxiety and depression symptoms. Conclusions: Within the limitations of this study, results suggest that central dysregulation or trigeminocervical convergence mechanisms are implicated in the process of pain-related TMD chronification and highlight the relevance of considering disability when defining CP-TMDs.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".