Factors affecting finger and hand pain in workers with HAVS
Bibliographic record
Abstract
BACKGROUND: Pain and its management are important aspects of hand-arm vibration syndrome (HAVS). AIMS: To determine the factors associated with finger and hand pain in workers with HAVS and, specifically, to assess the impact of several neurological variables as well as the vascular component of HAVS, grip strength and age. METHODS: We assessed men with HAVS at a hospital occupational medicine clinic over 2 years. Subjects scored finger and hand pain separately using the Borg Scale (0-10). The possible predictors we evaluated included the Stockholm Neurological Scale (SNS) and Stockholm Vascular Scale (SVS) stages, current perception threshold (CPT), carpal tunnel syndrome (CTS), ulnar neuropathy, grip strength and age. We carried out nerve conduction testing to confirm the presence of CTS and ulnar neuropathy and measured CPT in the fingers at 2000 Hz, 250 Hz and 5 Hz corresponding to A-beta (large myelinated), A-delta (small myelinated) and C (unmyelinated) fibres, respectively. We calculated Spearman rank correlations to examine the relation between finger and hand pain and possible predictor variables. RESULTS: Among the 134 subjects, the median (25th-75th percentile) pain scores were 6 (4-8) for the fingers and 5 (1-7) for the hands. We found statistically significant correlations with finger pain for the SVS stage (r = 0.239; P < 0.01) and CTS (r = 0.184; P < 0.05). The only statistically significant correlation identified for hand pain was a negative correlation with grip strength (r = -0.185; P < 0.05). CONCLUSIONS: Management of finger and hand pain in HAVS should focus on the correlates we have identified.
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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 | 0.002 |
| 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) | 0.003 | 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".