Current Research and Development in the Field of Magnetic Resonance Contrast Media
Bibliographic record
Abstract
The next-generation, high relaxivity, gadolinium-based contrast agents (GBCAs) are discussed, together with new studies of safety, improvements in MR technique, and the ongoing development of additional agents. It is likely that the next generation agents, gadopiclenol and gadoquatrane, will largely replace the current standards, the macrocyclic gadolinium chelates, despite the excellent safety profile and very high stability of the latter. In the Group of Seven (G7) nations, which includes Canada, France, Germany, Italy, Japan, the United Kingdom and the United States, use of the linear gadolinium chelates has largely ceased, due to concerns regarding their relative instability as compared to the macrocyclic agents and the deposition of gadolinium that occurs in many tissues, including brain and bone, following their injection. Manganese-based compounds are once again being investigated, a field largely untouched since the initial development of clinical MR contrast media in the 1980s. Their potential impact on clinical imaging is, however, unclear. New information continues to emerge regarding differences in stability of the gadolinium-based agents. Artificial intelligence and deep learning techniques are maturing and are discussed briefly, given their potential and recent clinical application involving MR contrast media.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".