Brain MRIs make up the bulk of the gadolinium footprint in medical imaging
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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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".