The Relationship between Rate of Algometer Application and Pain Pressure Threshold in the Assessment of Myofascial Trigger Point Sensitivity
Bibliographic record
Abstract
BACKGROUND: Pressure algometry is a commonly employed technique in the assessment of both regional and widespread musculoskeletal pain. Despite its acceptance amongst clinicians and scientists, the relationship between rate of pressure application (RoA) and pain pressure threshold (PPT) remains poorly understood. We set out to test the hypothesis that a strong, positive, linear relationship exists between the RoA and the PPT within the infraspinatus of young healthy subjects. METHODS: Thirty-three participants were randomly recruited from the local university community. PPT measures were recorded from a clinically identified myofascial trigger point within the right infraspinatus muscle during pressure algometry. A total of 2 PPT measures were recorded using each of 3 different RoAs, including low (15 N/s), medium (35 N/s), and high (55 N/s). Three baseline trials were also conducted at 30 N/s. The Pearson's correlation coefficient between RoA and PPT was calculated for each subject and averaged across participants. RESULTS: The mean(SD) correlation between subjects was 0.77 (0.19), and the mean (SD) slope of the linear regression was 0.13 (0.09). CONCLUSION: Our results demonstrate that there is a strong, linear relationship between the RoA and PPT when using the pressure algometry technique. The low slope between RoA and PPT suggests clinicians can rely on PPT assessments despite small RoA fluctuations. Future research should explore this relationship further in a clinical population and in other muscles affected by chronic myofascial pain. Advancing cost-effective, reliable, and clinically feasible tools such as algometry is important to enhancing the diagnosis and management of chronic myofascial pain.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".