Bibliographic record
Abstract
Background: Systemic sclerosis (SSc) is a vasculopathy with increased tissue deposition of collagen. The aetiology is unknown. Genetic and environmental susceptibility factors have been implicated. It is unknown whether disease presentation varies within Europe. Aims and Methods: The baseline data of all SSc patients entered in the EULAR Scleroderma Trials and Research (EUSTAR) database up to April 2007 were analysed for geographical differences with regard to organ involvement, and geographical clusters with regard to clinical subsets (diffuse vs limited SSc) and autoantibodies (anticentromere vs anti-Scl70). Results: 3661 patients from 79 centres in 62 cities and 23 countries were analysed. There was no clear trend between geographical coordinates and SSc subsets, although there appeared to be an increased prevalence of Scl70 in the more eastern centres. There was no association between geographical longitude or latitude and the age at the onset of Raynaud’s phenomenon or the onset of non-Raynaud’s symptoms. There was also a trend for the more eastern centres to care for patients with a higher prevalence of more severe organ manifestations (pulmonary arterial hypertension, cardiac involvement). Between different centres within one city there was a large variability in the frequency of organ complications. Conclusion: This analysis suggests that eastern centres care for more severe SSc manifestations in Europe. Large differences in patient referral account for a large local variability of SSc presentations and preclude the identification of genetic or environmental factors.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.238 | 0.128 |
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".