The Frequency of Scleroderma Renal Crisis over Time: A Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) leads to a high mortality from internal organ involvement. Scleroderma renal crisis (SRC) usually occurs in the diffuse cutaneous SSc (dcSSc) subset early in the disease, often with acute severe hypertension and renal failure. Prevalence of SRC since its classification in the early 1970s was determined in publications to assess whether the prevalence of SRC has changed over time because the proportion with the dcSSc subset is smaller in contemporary cohorts. METHODS: A review of the literature was conducted up to May 2015 using the PubMed, EMBASE, CINAHL, and Cochrane Library databases. Articles were included if they mentioned the prevalence of SRC and were cohort or cross-sectional studies with 50 or more patients with SSc. Articles were excluded if they were not in English or were a case series of SRC or case-control studies. RESULTS: Of the 5317 citations identified, 22 qualified. Years of publication were from 1983 to 2011, and cohort size varied from 68 to 8554 patients with SSc totaling 21,908 patients (9248 with dcSSc, 42%). There was no statistical reduction in the temporal prevalence of SRC noticed in the overall patients (4%), patients with dcSSc (7%-9%), or patients with limited cutaneous SSc (lcSSc; 0.5%-0.6%) based on either the start date of the cohort or publication date. CONCLUSION: It appears that SRC remains uncommon in lcSSc and the rate in the dcSSc group may be stable over time. However, increasing awareness of SRC could lead to higher rates in more recent years and/or better survival from SRC, but this was not observed.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.038 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".