The Influence of Different Solvents on Systemic Sclerosis
Bibliographic record
Abstract
BACKGROUND: Several studies have collected detailed data to examine which specific solvents account for the association between solvents and risk of systemic sclerosis (SSc). These studies generally reported elevated risks associated with many of the specific solvents examined, such as toluene, xylene, and trichloroethylene. The previous meta-analysis was not able to conduct separate analyses for specific solvent subtypes. OBJECTIVE: The aims of the new meta-analysis were to investigate a more comprehensive estimate and to consider the effect of different solvents on SSc. METHODS: We searched PubMed, Biosis Previews, China National Knowledge Infrastructure, and Wanfang for all articles published before July 2015. Fourteen case-control studies (1657 patients and 3838 controls) were included. The quality of studies was scored according to the Newcastle-Ottawa scale. The final odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by a fixed- or random-effects model according to heterogeneity test. Publication bias was assessed using Begg test. RESULTS: The risk of SSc was significantly different among sex, age, and exposure assessment methods. Separate analyses for specific solvent subtypes indicated that SSc was associated with aromatic solvents (OR, 2.72; 95% CI, 1.21-6.09), trichloroethylene (OR, 2.07; 95% CI, 1.34-3.17), halogenated solvents (OR, 1.49; 95% CI, 1.12-1.99), and ketones (OR, 4.20; 95% CI, 2.19-8.06). CONCLUSIONS: Exposure to identified types solvents does seem to be a risk factor for developing SSc. Needed efforts to decrease such exposures are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".