Bibliographic record
Abstract
Gadolinium-based contrast agents have been used for magnetic resonance imaging (MRI) examinations since the late 1980s with an excellent overall cumulative safety record. Initially favored for use in patients with renal impairment because of lack of significant nephrotoxic effect at clinical doses, in 2006, multiple reports convincingly linked the rare but serious disease nephrogenic systemic fibrosis to the administration of gadolinium-based contrast agents in patients with severe renal failure. This in turn led to new policies on administration of these agents, resulting in changes in practice patterns that have virtually resulted in the elimination of the disease after the year 2009. The purpose of this review is to summarize the factors that led to the emergence of nephrogenic systemic fibrosis, including the risk associated with different types of contrast agents based on their stability, and the changes in practice patterns and usage of gadolinium-based contrast agents in recent years that have been mainly driven by the discovery and association with nephrogenic systemic fibrosis. The article will conclude with a brief overview of new emerging safety concerns that could further impact the use of this class of contrast agents and impact practice patterns in the future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".