Scleroderma Renal Crisis: Still a Lot To Do
Bibliographic record
Abstract
Scleroderma renal crisis (SRC) in recent literature history has been superseded by other major organ involvement (heart, lungs) as the leading cause of death in systemic sclerosis (SSc)1. Despite this epidemiological shift, it remains one of the most serious and life-threatening complications regarding its individual effect on patients, with the added difficulty that research with randomized clinical or prospective studies is extremely difficult in its setting2. Another issue concerns the definition of SRC, which is highly heterogeneous in the literature. A recent systematic literature review3 pointed out that practically every paper of the more than 40 included in the review used its own definition of SRC, both in the core characteristics (new-onset arterial hypertension, new and rapidly progressive acute kidney injury) and in the listing of the possible accompanying symptoms: hypertensive encephalopathy, seizures, thrombotic microangiopathy with anemia, and thrombocytopenia. There are diagnostic changes at the kidney biopsy, but there is no precise indication for it. Kidney biopsy appears necessary when differential diagnoses are not formally ruled out with … Address correspondence to Prof. C. Montecucco, Unit of Rheumatology, University of Pavia, IRCCS Policlinico San Matteo Foundation, Piazzale Golgi 19, 27100 Pavia, Italy. E-mail: montecucco{at}smatteo.pv.it
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".