Evaluation of neurological testing for hand–arm vibration syndrome
Bibliographic record
Abstract
BACKGROUND: The neurological component of hand-arm vibration syndrome (HAVS) uses the Stockholm Workshop Scale sensorineural (SWS SN) stages for classification. Proximal compressive neuropathies are common in HAVS and the symptoms are similar to SN HAVS. The SWS may not be a valid staging tool if a patient has comorbid proximal compression neuropathy. AIMS: To evaluate the prevalence of proximal compression neuropathy in patients presenting for HAVS assessment and examine the association between compressive neuropathies and SWS SN. METHODS: A standardized assessment protocol was used to assess 431 patients for HAVS at St. Michael's Hospital, Toronto, Ontario. The prevalence of median and ulnar compressive neuropathies was determined. The association between proximal compression neuropathies and SWS SN stage (0/1 versus 2/3) was evaluated using Chi-square and Fisher's exact tests as well as multivariable logistic regression. RESULTS: Most patients (79%) reported numbness and 20% had reduced sensory perception (SWS SN Stage 2/3). Almost half (45%) had median neuropathy at the wrist and 7% had ulnar neuropathy. There was no association between the SWS SN stage and median or ulnar neuropathy. CONCLUSIONS: Two neurological lesions should be investigated in patients presenting for HAVS assessment: compressive neuropathy and digital neuropathy. The prevalence of compressive neuropathies is high in patients being assessed for HAVS and therefore nerve conduction studies (NCS) should be included in HAVS assessment protocols. Comorbid proximal neuropathy does not affect the SWS SN stage; therefore, NCS and SWS SN seem to be measuring different neurological outcomes in HAVS patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 0.001 |
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".