Diagnostic Accuracy of Clinical Tests for Neurogenic and Vascular Thoracic Outlet Syndrome: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To summarize the evidence on the accuracy of clinical tests to help confirm or refute a diagnosis of thoracic outlet syndrome (TOS). METHODS: We searched 10 databases (January 1990 to February 2016) using relevant key words and medical subject headings terms. We considered diagnostic test accuracy studies comparing clinical tests for the diagnosis of TOS against a reference test. Cross-sectional, cohort, and case-control studies and randomized controlled trials were included. Risk of bias was appraised using QUADAS-2 and the Quality Appraisal of Reliability Studies checklist. We performed a qualitative synthesis of scientifically admissible studies. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline was used to report findings. RESULTS: A total of 3932 articles were retrieved. After removal of duplicates, 1767 articles were screened for titles and abstract, leaving 494 articles for full-text review. Ten studies met the eligibility criteria and were assessed for risk of bias, 4 of which were included in the review. None of the included studies used the same index tests when comparing with a gold standard, and quality was poor. High clinical heterogeneity and the use of different comparators prevented from pooling results. Findings suggest that prescribing magnetic resonance imaging during provocative positioning to confirm a diagnosis of TOS may be useful. However, this is associated with a high false-positive rate of venous compression. CONCLUSION: Little evidence currently supports the validity of clinical tests for the diagnosis of TOS. Future diagnostic accuracy studies should aim to use established methodological criteria and appropriate reporting guidelines to help validate clinical tests for diagnosing patients with TOS.
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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 | 0.021 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".