Human temporomandibular joint and myofascial pain biochemical profiles: a case‐control study
Bibliographic record
Abstract
Neurobiological mechanisms of human musculoskeletal pain are poorly understood. This case-control study tested the hypothesis that biomarkers within temporomandibular muscle and joint disorders (TMJD) subjects' masseter muscles or temporomandibular joint (TMJ) synovial fluid correlate with plasma biomarker concentrations. Fifty subjects were recruited and categorized into TMJD cases (n=23) and pain-free controls (n=27) at the University of Minnesota School of Dentistry. Prior to specimen collection, pain intensity and pressure pain threshold masseter muscles and the TMJs were assessed. We collected venous blood; biopsied masseter muscle; and sampled TMJ synovial fluid on the subjects' side of maximum pain intensity. We assayed these tissues for the presence of nerve growth factor (NGF), bradykinin (BK), leukotreine B(4) (LTB(4) ) and prostaglandin E(2) (PGE(2) ), F(2) -isoprostane (F(2) I) and substance P (SP). The data was analyzed using Spearman Correlation Coefficients. We found that only plasma concentrations of bradykinin statistically correlated with synovial fluid concentrations (ρ=-0·48, P=0·005), but no association was found between pain intensities. The data suggests that biomarkers used to assess TMJD need to be acquired in a site-specific manner. We also discovered that F(2) I concentrations were associated with muscle pain intensity and muscle pressure pain threshold (PTT) (β=0·4, 95%CI: 0·03-0·8) and joint PPT (β=0·4, 95%CI: 0·07-0·8) suggesting that muscle oxidative stress is involved in myofascial pain and that F(2) -I may be a biomarker for myofascial pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".