Factors affecting finger and hand pain in workers with HAVS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it