Gadolinium detection via<i>in vivo</i>prompt gamma neutron activation analysis following gadolinium-based contrast agent injection: a pilot study in 10 human participants
Bibliographic record
Abstract
Gadolinium (Gd) based contrast agents are routinely used as part of many magnetic resonance imaging (MRI) procedures. The widespread use of these agents and concerns about Gd toxicity, motivated us to develop a monitoring procedure that could non-invasively measure quantitatively potential retention of toxic free Gd in tissues after use of the agent. We have been developing a method to measure Gd painlessly and non-invasively by prompt gamma neutron activation analysis. In this paper we present the results of a pilot study where we show that we can measure Gd, quantitatively in vivo, in the lower leg muscle of 10 participants. A series of three neutron leg scans were performed. The effective radiation dose for a single neutron leg scan was very low, 0.6 µSv, so multiple scans were possible. Calibration phantom and in vivo detection limits were determined to be identical: 0.58 ppm. Gd was not detectable in muscle prior to exposure to the contrast agent Gadovist(®). Gd was detected, at greater than 99% confidence, in 9 participants within 1 h of contrast administration and in 1 participant approximately 3.3 h post-contrast administration. The measured concentrations of Gd ranged from 2.0 to 17.3 ppm (6.9 to 56 uncertainties different from zero). No detectable Gd was measured in any participant in the third neutron scan (conducted 0.7 to 5.9 d post-contrast). The results of this study validate our new measurement technology. This technique could be used as a non-invasive monitoring procedure for exposure and retention of Gd from Gd-based chelates used in MRI.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".