Diagnostic Applications of Ultrasonography in Myofascial Trigger Points: A Scoping Review and Critical Appraisal of Literature
Bibliographic record
Abstract
Objective: Myofascial trigger points (MTrPs) are pathognomonic of myofascial pain syndrome. The detection ability of MTrPs via ultrasonography is underreported and the characteristics of MTrPs are not sufficiently standardized. The objective was to summarize the characteristics and diagnostic abilities of ultrasonography for MTrP investigations. Materials and Methods: A multi-database, and bibliography hand-search was implemented. Studies of ≥10 patients, published after 1980, appraising ultrasonography as a diagnostic aid for myofascial pain syndrome were included. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used to evaluate the diagnostic accuracy of the included studies. Results: Six cross-sectional studies met the inclusion criteria. The back and trapezius muscles were the most studied locations. The diagnostic studies were of low-medium risk of bias. The studies reported a large range of diagnostic metrics (accuracy 58%–100%, sensitivity 33%–91%, specificity 75%–100%, positive predictive value 91%–100%, negative predictive value 47%–97%, positive likelihood ratio 3.6, and negative likelihood ratio 0.12–0.67). Conclusion: This review found low-medium risk of bias evidence in support of ultrasonography for MTrP investigations. The clinical studies identified in the scoping review used gray-scale ultrasound equipment systems with a 5 to 14 MHz transducer to diagnose MTrPs and the local twitch response, and MTrPs were visualized mostly as hypoechoic nodules.
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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 | 0.038 | 0.144 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.048 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".