Exploring cold sensitivity among workers with hand-arm vibration syndrome
Bibliographic record
Abstract
BACKGROUND: Previous research on functional impairments due to vascular effects in workers with hand-arm vibration syndrome (HAVS) has focused on the upper extremities with little attention to the lower extremities. AIMS: To examine the severity and factors associated with cold intolerance and sensitivity in the hands and feet of workers with HAVS. METHODS: A survey and retrospective chart review were used to collect demographics, work conditions and disease characteristics on workers undergoing HAVS assessments. Data also included workers' scores on the Cold Intolerance Symptom Severity (CISS) questionnaire, which was adapted to evaluate the upper and lower extremities separately. Associations between upper and lower extremity scores were explored using Spearman correlations and Wilcoxon signed rank tests, and regression models were used to evaluate whether variables of interest predicted CISS total scores. RESULTS: Participants (N = 93; 55% response rate) worked primarily in mining and construction, were exclusively male, and had a mean age of 48 years. Cold intolerance and sensitivity were commonly reported in the feet and correlated with hand symptoms, but most CISS individual questions and total scores were significantly higher for the hands (versus feet). Regression models indicated that QuickDASH and Stockholm vascular scale scores significantly predicted CISS total scores for the hands, while exposure to foot vibration, plethysmography severity scores of the feet and QuickDASH scores significantly predicted CISS total scores for the feet. CONCLUSIONS: Clinicians should assess and treat cold intolerance and sensitivity in both the hands and feet of workers with HAVS to improve workers' daily functioning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".