Upper extremity disability in workers with hand-arm vibration syndrome
Bibliographic record
Abstract
BACKGROUND: Hand-arm vibration syndrome (HAVS) is a common occupational problem and it is important to understand the disability associated with this condition. AIMS: To measure upper extremity disability using the disabilities of the arm, shoulder and hand (DASH) questionnaire in workers with HAVS and to determine how this disability is affected by the vascular and neurological components of HAVS and other factors, in particular musculoskeletal variables. METHODS: Subjects were recruited from HAVS patients assessed at St Michael's Hospital, Toronto, Canada, over a 2-year period. All participants were assessed by an occupational medicine specialist to determine the specific components of HAVS and musculoskeletal variables including upper extremity pain score measured by the Borg scale. The DASH questionnaire was completed on the same day as the clinical assessment and before any feedback had been given about the clinical findings. RESULTS: A total of 141 workers with HAVS were recruited and 139 agreed to participate in the study. This study group had a statistically significantly higher mean DASH score than the US population (P < 0.001). The multiple linear regression analysis indicated that upper extremity pain score (P < 0.001), the Stockholm sensorineural scale (P < 0.01) and the number of fingers blanching (P < 0.05) had a statistically significant association with an increase in the DASH score. The highest partial R(2) value was for the upper extremity pain score. CONCLUSIONS: Workers with HAVS have significant upper extremity disability and musculoskeletal factors appear to make an important contribution to this disability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".