Scratch Collapse Test for Carpal Tunnel Syndrome: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Despite the fact that carpal tunnel syndrome (CTS) is the most common entrapment neuropathy, the diagnostic accuracy of clinical screening examinations for CTS is controversial. The scratch collapse test (SCT) is a novel test that may be of diagnostic advantage. The purpose of our study was to determine the diagnostic accuracy of the SCT for CTS. METHODS: A literature search was performed using PubMed (1966 to April 2018); Ovid MEDLINE (1966 to April 2018); EMBASE (1988 to April 2018); and Cochrane Central Register of Controlled Trials (The Cochrane Library, to April 2018). We examined the studies for the pooled sensitivity, specificity, and likelihood ratios of the SCT. This review has been registered with PROSPERO (CRD42018077115). RESULTS: The literature search generated 13 unique articles. Seven articles were included for full text screening and 3 articles met our inclusion criteria, all of which were level II evidence with low risk of bias (165 patients). Pooled sensitivities, specificities, positive likelihood ratio, and negative likelihood ratios were 0.32 [95% CI (0.24-0.41)], 0.62 [95% CI (0.45-0.78)], 0.75 [95% CI (0.33-1.67)], and 1.03 [95% CI (0.61-1.74)], respectively. The calculated area under the summary receiver operating characteristic (AUSROC) curve was 0.25, indicating a low diagnostic accuracy. CONCLUSION: The SCT has poor sensitivity; however, it is moderately specific. Based on the current literature and their variable quality of the evidence, we conclude that the SCT is not an adequate screening test for detecting CTS.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".