Bibliographic record
Abstract
PURPOSE OF REVIEW: Systemic sclerosis (SSc) has a case-based mortality that is one of the highest among the rheumatic diseases. This article is an appraisal of current knowledge regarding survival, causes of death and risk factors for reduced life-expectancy in systemic sclerosis (SSc). RECENT FINDINGS: Recent systematic reviews of cohorts studies published worldwide have revealed a pooled standardized mortality ratio in SSc of 3.5, and reiterated the importance of heart-lung involvement as a major cause of death in this disease. Indeed, the pooled hazard ratio (HR) of mortality in SSc patients with pulmonary arterial hypertension (PAH) compared with those without is 3.5, while the pooled HR for mortality in those with interstitial lung disease is 2.6. The average life expectancy of patients with SSc is 16-34 years less than age-matched and sex-matched population peers. Current research efforts are focused on quantifying early as well as late mortality, and modeling for predictors of death in SSc, with the ultimate goal of attenuating this risk and improving survival, as new therapies emerge. SUMMARY: Studies have consistently shown a substantially increased mortality in SSc, predominantly due to cardio-pulmonary complications. A better understanding of risk factors for mortality holds the promise of improving outcomes in this devastating multiorgan autoimmune disease.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".