Improving characterization and diagnosis quality of myofascial pain syndrome: a systematic review of the clinical and biomarker overlap with delayed onset muscle soreness
Bibliographic record
Abstract
INTRODUCTION: Myofascial pain syndrome (MPS) is one of the most common conditions of chronic musculoskeletal pain, yet its mechanisms are still poorly understood. Delayed Onset Muscle Soreness (DOMS) is also a regional pain syndrome that has clinical similarities to MPS, but has been better investigated. Emerging research suggests that DOMS may be a valid experimental model for studying MPS; however, a comparison of the similarities and differences of these two conditions has previously not been performed. Herein, we aimed to identify the similarities and differences in the clinical features and biomarkers between DOMS and MPS in order to better define MPS and identify future areas of (DOMS-informed) MPS research. EVIDENCE ACQUISITION: In order to identify similarities and differences in the clinical manifestation and biomarkers of DOMS and MPS, scoping literature searches were performed using Medline (1965-2019), Embase (1966-2019) and Central (1966-2019) databases. Fifty-three full-text articles were reviewed out of the 2836 articles retrieved in the search. EVIDENCE SYNTHESIS: A scoping review of the literature demonstrated that DOMS and MPS similarly present as conditions of musculoskeletal pain that are associated with decreased strength and limited range of motion. However, while taut bands and discrete tender spots were described in DOMS, none of the studies reviewed have characterized whether these tender points represent the classic myofascial trigger point phenomenon observed in MPS. Certain systemic circulation biomarkers, including inflammatory cytokines and growth factors, were commonly elevated in MPS and DOMS; further research is needed to determine if other biomarkers that are currently characterized in DOMS are useful to enhance the clinical evaluation of MPS. CONCLUSIONS: DOMS and MPS share clinical and biomarker similarities suggesting that DOMS may be a useful model for studying MPS.
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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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".