Toward Understanding of Environmental Risk Factors in Systemic Sclerosis
Bibliographic record
Abstract
Importance Systemic sclerosis (SSc) is a severe, chronic, and incurable autoimmune fibrotic skin disease with significant extracutaneous involvement. Low concordance rate in twin studies and unequal geographic distribution of SSc argues for importance of environment in disease initiation and progression. Objective In this manuscript we provide a summary of all investigated potential external risk factors for SSc. Data sources A literature search in PubMed and EMBASE database was performed for studies published until January 1, 2020 by 2 reviewers (EN and LO) independently. Findings Occupational and/or environmental exposures to silica and organic solvents are associated with increased incidence and severity of SSc. Exposure to epoxy resins, asbestos, and particulate air pollution favors increased risk of SSc, but data are based on limited number of observational studies. There is insufficient evidence to conclude an association between SSc development and other occupational (eg, welding fumes) or personal exposures (eg, smoking, vitamin D deficiency). Association of SSc with silicone breast implants has been disproven. Infectious pathogens (eg, Helicobacter pylori and angiotropic viruses) and dysbiosis seem to play a role in SSc development and severity, but their role remains to be clarified. Conclusions and relevance It may be prudent to counsel our patients with SSc (or those at risk of SSc) to avoid occupations with exposure to silica, organic solvents, asbestos and epoxy resins; restraint from smoking, using cocaine or drugs with pro-fibrotic potential. While the association between low vitamin D and SSc remains to be confirmed, we believe that SSc patients should be encouraged to maintain healthy vitamin D levels as benefits outweigh the risks.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".