Occupational exposure to solvents and gender-related risk of systemic sclerosis: a metaanalysis of case-control studies.
Bibliographic record
Abstract
OBJECTIVE: In 2001 a metaanalysis reported an excess risk of systemic sclerosis (SSc) related to solvents exposure. The magnitude of risk varied among studies and sources of heterogeneity have not been investigated due to a lack of statistical power. We conducted a new metaanalysis to identify features associated with the magnitude of SSc risk in patients exposed to solvents. METHODS: We searched 4 databases (Medline, Pascal, Pascal Biomed, Francis). Inclusion criteria were: case-control study, occupational exposure to solvents (OES) assessed by questionnaire and summarized to "any solvent" or "any organic solvent," SSc defined by the American College of Rheumatology or the consultant's criteria. The quality of studies within this metaanalysis was scored according to the Newcastle-Ottawa scale. Odds ratios (OR) were adjusted for the "publication bias" and validated by a sensitivity analysis. Subgroup analyses investigated the effect of gender, quality of studies, and the type of controls. RESULTS: Among 11 studies (1291 patients and 3435 controls), 9 involved a majority of women (76.2 to 100%), while 2 involved men only. The risk of SSc associated with OES was variable among studies (p for heterogeneity = 0.01) and overrepresentation of higher OR values in smaller studies (p = 0.003) suggested "publication bias." SSc was associated with OES (OR 2.4; 95% CI 1.7-3.4; p < 0.0001), including after adjusting for bias (OR 1.8; 95% CI 1.2-2.5; p = 0.002). The relative risk was higher (p = 0.03) in men (OR 3.0; 95% CI 1.9-4.6; p < 0.0001) than in women (OR 1.8; 95% CI 1.5-2.1; p < 0.0001). CONCLUSION: Whereas SSc affects women predominantly, among subjects with occupational exposure to solvents, men are at higher risk than women for the disease.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".