Diagnostic Accuracy of Scalene and Pectoralis Minor Muscle Blocks for Thoracic Outlet Syndrome: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Thoracic outlet syndrome (TOS) is a complex neurovascular condition that remains challenging to diagnose, particularly neurogenic TOS (nTOS), which comprises most cases. While vascular TOS has clear diagnostic criteria, nTOS diagnosis relies on clinical assessments, imaging, and electrophysiologic studies. Scalene and pectoralis minor muscle blocks have been proposed as diagnostic tools, but their accuracy remains uncertain. Objective This systematic review and meta-analysis assesses the pooled sensitivity, specificity, and diagnostic accuracy of scalene and pectoralis minor blocks for TOS. Methods A systematic literature search was performed across PubMed, Embase, Scopus, Cochrane Library, Web of Science, and Google Scholar following PRISMA guidelines. Studies evaluating the diagnostic accuracy of these blocks for TOS were included. The QUADAS-2 and Newcastle-Ottawa Scale were used for quality assessment. A meta-analysis using RevMan and STATA assessed pooled sensitivity, specificity, and diagnostic odds ratios (DORs). Results Of the 180 reports yielded by the search, 12 studies met inclusion criteria (950 patients). Pooled sensitivity for scalene and pectoralis minor blocks was 87% (95% CI: 83%-90%), while specificity was 34% (95% CI: 26%-43%). The diagnostic odds ratio was 3.98 (95% CI: 2.50-6.34). Substantial heterogeneity was observed (I 2 = 68%, P < 0.001), attributed to variations in injection protocols, outcome definitions, and patient selection. Conclusion Scalene and pectoralis minor blocks have high sensitivity but low specificity for TOS diagnosis. Their use as stand-alone diagnostic tools is limited. However, they may be valuable within a multimodal diagnostic framework integrating clinical evaluation, imaging, and electrophysiologic testing.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".