Vasospasm in the feet in workers assessed for HAVS
Bibliographic record
Abstract
BACKGROUND: Previous studies have suggested that the presence of the vascular component of hand-arm vibration syndrome (HAVS) in the hands increases the risk of cold-induced vasospasm in the feet. AIMS: To determine if objectively measured cold-induced vasospasm in the hands is a risk factor for objectively measured cold-induced vasospasm in the feet in workers being assessed for HAVS. METHODS: The subjects were 191 male construction workers who had a standardized assessment for HAVS including cold provocation digital photocell plethysmography of the hands and feet to measure cold-induced vasospasm. Bivariate analysis and multinomial logistic regression were used to examine the association between plethysmographic findings in the feet and predictor variables including years worked in construction, occupation, current smoking, cold intolerance in the feet, the Stockholm vascular stage and plethysmographic findings in the hands. RESULTS: Sixty-one (32%) subjects had non-severe vasospasm and 59 (31%) had severe vasospasm in the right foot with the corresponding values being 57(30%) and 62 (32%) in the left foot. Multinomial logistic regression indicated that the only statistically significant predictor of severe vasospasm in the feet was the presence of severe vasospasm in the hands (OR: 4.11, 95% CI: 1.60-10.6, P < 0.01 on the right side and OR: 4.97, 95% CI: 1.82-13.53, P < 0.01 on the left side). Multinomial logistic regression analysis did not indicate any statistically significant predictors of non-severe vasospasm in the feet. CONCLUSIONS: Workers assessed for HAVS frequently have cold-induced vasospasm of their feet. The main predictor of severe vasospastic foot abnormalities is severe cold-induced vasospasm in the hands.
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.
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) | 0.002 | 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".