Measurement of gadolinium retention: current status and review from an applied radiation physics perspective
Bibliographic record
Abstract
OBJECTIVE: This article briefly reviews the main measurement techniques for the non-invasive detection of residual gadolinium (Gd) in those exposed to gadolinium-based contrast agents (GBCAs). Approach and Main results: The current status of in vivo Gd measurement is discussed and is put into the context of concerns within the radiology community. The main techniques are based on applied atomic/nuclear medicine utilizing the characteristic atomic and nuclear spectroscopic signature of Gd. The main emission energies are in the 40-200 keV region and require spectroscopic detectors with good energy resolution. The two main techniques, prompt gamma neutron activation analysis and x-ray fluorescence, provide adequate detection limits for in vivo measurement, whilst delivering a low effective radiation dose on the order of a few µSv. SIGNIFICANCE: Gadolinium is being detected in measureable quantities in people with healthy renal function who have received FDA approved GBCAs. The applied atomic/nuclear medicine techniques discussed in this review will be useful in determining the significance of this retention, and will help on advising future administration protocols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".