Work‐related cumulative trauma disorders of the upper extremity: Navigating the epidemiologic literature
Bibliographic record
Abstract
BACKGROUND: Cumulative trauma disorder of the upper extremity (CTDUE) is an umbrella term used to describe disorders resulting from repeated use of the upper extremity over time rather than a specific incident. The primary purpose of this article is to summarize the literature regarding the rate of work-related CTDUE, while drawing attention to the various factors contributing to the wide range of reported findings. METHODS: The Cumulative Index to Nursing and Allied Health and Medline databases were searched for articles focusing on etiology or rates of occurrence of work-related CTDUE and their findings were summarized. RESULTS: Potential reasons for rising rates, a gender differential, and the substantial range in rates and rate ratios are delineated and important factors to consider when interpreting rates derived from workers' compensation data are detailed. CONCLUSIONS: Future research should attempt to correctly identify more specific categories of CTDUE in well-defined and accurately-quantified "at risk" populations to provide more meaningful information regarding the epidemiology of CTDUE and the effectiveness of control activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".