Hand-arm vibration syndrome: What family physicians should know.
Bibliographic record
Abstract
OBJECTIVE: To provide family physicians with an understanding of the epidemiology, pathogenesis, symptoms, diagnosis, and management of hand-arm vibration syndrome (HAVS), an important and common occupational disease in Canada. SOURCES OF INFORMATION: A MEDLINE search was conducted for research and review articles on HAVS. A Google search was conducted to obtain gray literature relevant to the Canadian context. Additional references were obtained from the articles identified. MAIN MESSAGE: Hand-arm vibration syndrome is a prevalent occupational disease affecting workers in multiple industries in which vibrating tools are used. However, it is underdiagnosed in Canada. It has 3 components-vascular, in the form of secondary Raynaud phenomenon; sensorineural; and musculoskeletal. Hand-arm vibration syndrome in its more advanced stages contributes to substantial disability and poor quality of life. Its diagnosis requires careful history taking, in particular occupational history, physical examination, laboratory tests to rule out alternative diagnoses, and referral to an occupational medicine specialist for additional investigations. Management involves reduction of vibration exposure, avoidance of cold conditions, smoking cessation, and medication. CONCLUSION: To ensure timely diagnosis of HAVS and improve prognosis and quality of life, family physicians should be aware of this common occupational disease and be able to elicit the relevant occupational history, refer patients to occupational medicine clinics, and appropriately initiate compensation claims.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".