Nephrogenic systemic fibrosis: A nephrologist's perspective
Bibliographic record
Abstract
Nephrogenic systemic fibrosis (NSF) is a debilitating disorder that affects patients with renal insufficiency. Recent evidence suggests that the development of NSF may be related to the administration of gadolinium-based contrast media (GBCM) in the setting of magnetic resonance imaging. As no treatment has consistently been effective in the management of NSF, strategies to prevent the development of this condition appear to be the best therapy. Identification of patients at greatest risk for developing NSF after exposure to GBCM is crucial. Risk factors include advanced chronic kidney disease (stages 4 and 5) and acute or chronic inflammatory events. The United States Food and Drug Administration has updated its public health advisory to include patients with moderate renal insufficiency (chronic kidney disease stage 3) as being at risk for developing NSF. However, these data require further verification and the vast majority of affected patients are already on renal replacement therapy. Another strategy in prevention may include consultation with a radiologist for imaging alternatives. If GBCM must be administered, immediate hemodialysis may be protective in patients already on hemodialysis; however, given the lack of data to support this, we do not recommend routine dialysis for patients not yet on dialysis or who are currently being treated with peritoneal dialysis. Decisions such as this should be made on a case by case basis after evaluating additional risk 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".