Diagnostic accuracy of sensory and motor tests for the diagnosis of carpal tunnel syndrome: a systematic review
Bibliographic record
Abstract
BACKGROUND: Carpal tunnel syndrome (CTS) is the most common entrapment mononeuropathy of the upper extremity. The previous systematic review of the diagnostic tests for CTS was outdated. The objective of this study was to compile and appraise the evidence on the accuracy of sensory and motor tests used for the diagnosis of CTS. METHODS: MEDLINE, CINAHL, and Embase databases were searched on January 20, 2020. Studies assessing at least one diagnostic accuracy property of the sensory or motor tests for CTS diagnosis were selected by two independent reviewers. Diagnostic test accuracy extension of the PRISMA guidelines was followed. Risk of bias and applicability concerns were rated using QUADAS-2 tool. Any reported diagnostic accuracy property was summarized. Study characteristics and any information on the accuracy of the sensory and motor tests for CTS diagnosis were extracted. RESULTS: We included sixteen clinical studies, assessing thirteen different sensory or motor tests. The most sensitive test for CTS diagnosis was the Semmes-Weinstein monofilament test (with 3.22 in any radial digit as the normal threshold) with sensitivity from 0.49 to 0.96. The tests with the highest specificity (Sp) were palmar grip strength (Sp = 0.94), pinch grip strength (Sp from 0.78 to 0.95), thenar atrophy (Sp from 0.96 to 1.00), and two-point discrimination (Sp from 0.81 to 0.98). CONCLUSIONS: The evidence was inconclusive on which sensory or motor test for CTS diagnosis had the highest diagnostic accuracy. The results suggest that clinicians should not use a single sensory or motor test when deciding on CTS diagnosis. TRIAL REGISTRATION: PROSPERO CRD42018109031 , on 20 December 2018.
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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 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".