International consensus criteria for diagnosing and staging hand–arm vibration syndrome
Bibliographic record
Abstract
PURPOSE: In the 30 years since the Stockholm Workshop Scale (SWS) was published, the scientific literature on hand-arm vibration syndrome (HAVS) has grown and experience has been gained in its practical application. This research was undertaken to develop an up-to-date evidence-based classification for HAVS by seeking consensus between experts in the field. METHODS: Seven occupational physicians who are clinically active and have had work published on HAVS in the last 10 years were asked to independently take part in a three-round iterative Delphi process. Consensus was taken when 5/7 (72%) agreed with a particular statement. Experts were asked to provide evidence from the literature or data from their own research to support their views. RESULTS: Consensus was achieved for most of the questions that were used to develop an updated staging system for HAVS. The vascular and neurological components from the SWS are retained, but ambiguous descriptors and tests without adequately developed methodology such as tactile discrimination, or discriminating power such as grip strength, are not included in the new staging system. A blanching score taken from photographs of the hands during vasospastic episodes is recommended in place of self-recall and frequency of attacks to stage vascular HAVS. Methods with the best evidence base are described for assessing sensory perception and dexterity. CONCLUSIONS: A new classification has been developed with three stages for the clinical classification of vascular and neurological HAVS based on international consensus. We recommend it replaces the SWS for clinical and research purposes.
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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 | 0.109 | 0.120 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.018 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".