The effect of excess body mass on the risk of carpal tunnel syndrome: a meta‐analysis of 58 studies
Bibliographic record
Abstract
We aimed to estimate the effects of overweight and obesity on carpal tunnel syndrome (CTS), and to assess whether sex modifies the associations. Literature searches were conducted in PubMed, Embase, Web of Science, Scopus, Google Scholar and ResearchGate databases from 1953 to February 2015. Fifty-eight studies consisting of 1,379,372 individuals qualified for a meta-analysis. We used a random-effects meta-analysis, assessed heterogeneity and publication bias, and performed sensitivity analyses. Overweight increased the risk of CTS or carpal tunnel release 1.5-fold (pooled confounder-adjusted odds ratio [OR] = 1.47, 95% CI 1.37-1.57, N = 1,279,546) and obesity twofold (adjusted OR = 2.02, 95% CI 1.92-2.13, N = 1,362,207). Each one-unit increase in body mass index increased the risk of CTS by 7.4% (adjusted OR = 1.074, 95% CI 1.071-1.077, N = 1,258,578). Overweight and obesity had stronger effects on carpal tunnel release than CTS. The associations did not differ between men and women, and they were independent of study design. Moreover, the associations were not due to bias or confounding. Excess body mass markedly increases the risk of CTS. As the prevalence of overweight and obesity is increasing globally, overweight-related CTS is expected to increase. Future studies should investigate whether a square-shaped wrist and exposure to physical workload factors potentiate the adverse effect of obesity on the median nerve.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.062 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".