Myofascial Pain Syndrome: A Narrative Review Identifying Inconsistencies in Nomenclature
Bibliographic record
Abstract
There is currently confusion surrounding the phenotype of and diagnostic criteria for myofascial pain syndrome (MPS) in the published literature. This narrative literature review investigated whether there is consensus regarding the descriptive terminology used for MPS and the trend of MPS publications over time. The phrase "myofascial pain syndrome" was used to search PubMed and Web of Science, returning 923 articles. Of these, we included only full-text, primary research articles containing "myofascial pain syndrome" in the title, reducing the total articles reviewed to 167. We identified 116 descriptors and categorized them under one of five clusters that shared similar findings and are commonly associated with MPS: "trigger points," "muscle," "pain," "nervous system," and "fascia." The frequency of the clinical criteria of Travell and Simons was tabulated. Terms pertaining to the clusters "trigger points," "muscle," or "pain" appeared in approximately 90% of the articles; "nervous system" in 46%; and "fascia" in 20%. Only 42% used the criteria of Travell and Simons. Most articles (122) included a combination of three or four clusters to describe MPS. In addition, MPS publications have doubled since 2010 compared to the prior decade. The publication patterns, determined by changes in which specialty journals articles on MPS have been published, have shifted from investigational to intervention studies. This may have been influenced by heterogeneity in the usage of MPS terminology. This underscores the lack of a reliable MPS diagnosis and limits human subjects research. Improved consistency in terminology is needed to establish consensus within the field and to inform future research studying the pathophysiology of 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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".