Brain MRIs make up the bulk of the gadolinium footprint in medical imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Assess the evolution of gadolinium consumption and magnetic resonance imaging (MRI) scanners in France and Western Brittany (France) and compare regional practices between public and private hospitals for each organ specialty. MATERIAL AND METHODS: We collected data from national and universal health registries, and Western Brittany's health care structures, between 2011 and 2018, about the number of MR imaging exams and machines, the number of delivered GBCAs (gadolinium-based contrast agents), prescriptions and administration protocols. RESULTS: Over the last eight years, we observed an increase in the number of MRI machines implemented in France (62%), correlated with the increase of annual gadolinium consumption (amount of delivered GBCAs in kg, 64%), without modification of the annual quantity of gadolinium used per machine (2.7kg in 2018). In Western Brittany, gadolinium impact is assigned to neuroimaging exams (50% CI95% [45;56] of all the contrast-enhanced exams), followed by thorax and abdomen exams (23% CI95% [18;28]). The ratio of injected exams to all exams is greater in public than in private hospitals (respectively 48% CI95% [46;49] versus 29% CI95% [26;30]). CONCLUSION: Gadolinium consumption is increasing, correlated with the increase in the number of examinations carried out. Regionally, the main impact comes from neuroimaging exams. No change in practices has been observed in recent years despite some warnings about gadolinium deposits and environmental consequences.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it