Risk factors for NSF: A literature review
Bibliographic record
Abstract
Emerging evidence linking gadolinium-based contrast agents (GBCAs) to nephrogenic systemic fibrosis (NSF) has changed medical practice patterns toward forgoing GBCA-enhanced magnetic resonance imaging (MRI) or substituting other imaging methods, which are potentially less accurate and often radiation-based. This shift has been based on reports of high NSF incidence at sites where a confluence of risk factors occurred in patients with severe renal dysfunction. This review article explores the factors that affect NSF risk, compares risks of alternative imaging procedures, and demonstrates how risk can be managed by careful selection of GBCA dose, timing of injection with respect to dialysis, and other factors. Nearly half of NSF cases are a milder form that does not cause contractures or reduce mobility. It appears that eliminating even a single risk factor can reduce NSF incidence/risk at least 10-fold. Elimination of multiple risk factors by using single-dose GBCA, dialyzing dialysis patients quickly following GBCA administration, avoiding GBCA in acute renal failure while serum creatinine is rising, and avoiding nonionic linear GBCA in renal failure patients may reduce NSF risk more than a thousand-fold, thereby allowing safe GBCA-enhanced MRI in virtually all patients. J. Magn. Reson. Imaging 2009;30:1298-1308. (c) 2009 Wiley-Liss, Inc.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".