Bibliographic record
Abstract
BACKGROUND: Raynaud's phenomenon, a common manifestation of the hand-arm vibration syndrome (HAVS), is typically diagnosed by a subjective history provided by employees. AIM: This study evaluates the validity of the subjective history of Raynaud's phenomenon provided by individuals applying for compensation for HAVS. METHODS: Thirty-six workers with a history of occupational hand-arm vibration exposure who were labelled as having Raynaud's phenomenon were asked to photographically document their finger symptoms before undergoing a detailed clinical assessment. Each individual was provided with a disposable camera and instructions. Returned photographs were reviewed for signs of Raynaud's phenomenon. The reliability of photograph interpretation was tested with three physicians and a non-physician. RESULTS: Inter and intra-rater reliability was very good, Kappa coefficient >0.80. Six individuals (17%) did not return cameras. Thirty individuals provided photographs and underwent a clinical evaluation. The photographs of 13 individuals (43%) did not show Raynaud's phenomenon and for four of these the diagnosis was not supported by careful symptom history. Seventeen individuals (57%) had photographic evidence of Raynaud's phenomenon. CONCLUSIONS: A presenting history of Raynaud's phenomenon in workers seeking compensation for HAVS may not be accurate since approximately half the cases are unable to provide objective photographic evidence of Raynaud's phenomenon.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".