The effect of hand-arm vibration syndrome on quality of life
Bibliographic record
Abstract
BACKGROUND: It is important to determine how hand-arm vibration syndrome (HAVS), a common occupational condition, affects quality of life (QOL). AIMS: To measure the physical (SF12-P) and mental (SF12-M) components of QOL in workers with HAVS, using the SF12 questionnaire, and to determine the effect of the vascular, sensorineural and musculoskeletal components of HAVS on QOL. METHODS: Subjects were recruited consecutively from workers with HAVS attending an occupational medicine clinic. They were assessed to determine the Stockholm vascular and sensorineural scale stages as well as an upper extremity pain score, measured by the Borg scale, as an indication of musculoskeletal problems associated with the use of vibrating tools. The SF12-P and SF12-M were both compared with Canadian population normal values after adjusting for age and sex. Multiple linear regression was used to determine the effect of the various HAVS components on SF12-P and SF12-M as well as the effects of age and carpal tunnel syndrome. RESULTS: One hundred and forty-one subjects were recruited and 139 (99%) agreed to participate, including 134 men and 5 women. The SF12-P and SF12-M scores were significantly below the Canadian population mean values (P < 0.001), indicating lower QOL. In the multiple regression analysis, the predictor with the largest partial R (2) value for both the SF12-P and SF12-M was the upper extremity pain score. CONCLUSIONS: Both the physical and the mental QOL in workers with HAVS were below Canadian population normal values and subjects' upper extremity pain score had the greatest effect on their QOL.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".