Raynaud’s Phenomenon in Medical Laboratory Workers Who Work with Solvents
Bibliographic record
Abstract
OBJECTIVE: To investigate whether there is an association between Raynaud's phenomenon (RP) and exposure to organic solvents in laboratory workers. METHODS: Technicians, scientists, and laboratory assistants working in histology, cytology, and transfusion medicine were surveyed about their use of solvents, particularly xylene and toluene, and about symptoms of RP. There were 341 responses. OR for having worked with solvents were calculated with logistic regression adjusted for age and sex. RESULTS: Laboratory workers who had worked with solvents had higher rates of severe RP, particularly those who had worked with xylene or toluene and either acetone (OR 8.8, 95% CI 1.9-41.1), or chlorinated solvents (OR 8.9, 95% CI 1.9-41.6), xylene or toluene and acetone compared to those who had worked with xylene or toluene but not acetone (OR 4.5, 95% CI 1.2-16.2), and similarly for chlorinated solvents (OR 4.5, 95% CI 1.2-16.3). RP symptoms occurring in the absence of cold exposure were more frequent for those who had worked with any solvent (OR 3.6, 95% CI 1.2-10.5) and just xylene or toluene (OR 2.8, 95% CI 1.1-7.3). Associations were also seen between increasing exposure to xylene or toluene and severe RP (OR 1.7, 95% CI 1.1-2.7, per 10 years) and with symptoms occurring in the absence of cold exposure (OR 1.7, 95% CI 1.2-2.5, per 10 years). CONCLUSION: We found that exposure to solvents may be associated with the development of RP, supporting previous work indicating that solvent exposure may be an etiological factor in systemic sclerosis.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".