Canadian Association of Radiologists Guidance on Contrast Associated Acute Kidney Injury
Bibliographic record
Abstract
Iodinated contrast media (ICM) is one of the most frequently administered pharmaceuticals. In Canada, over 5.4 million computed tomography (CT) examinations were performed in 2019, of which 50% were contrast enhanced. Acute kidney injury (AKI) occurring after ICM administration was historically considered a common iatrogenic complication which was managed by screening patients, prophylactic strategies, and follow up evaluation of renal function. The Canadian Association of Radiologists (CAR) initially published guidelines on the prevention of contrast induced nephropathy in 2007, with an update in 2012. However, new developments in the field have led to the availability of safer contrast agents and changes in clinical practice, prompting a complete revision of the earlier recommendations. This revised guidance document was developed by a multidisciplinary CAR Working Group of radiologists and nephrologists, and summarizes changes in practice related to contrast administration, screening, and risk stratification since the last guideline. It reviews the scientific evidence for contrast associated AKI and provides consensus-based recommendations for its prevention and management in the Canadian healthcare context. This article is a joint publication in the Canadian Association of Radiologists Journal and Canadian Journal of Kidney Health and Disease, intended to inform both communities of practice.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.042 | 0.021 |
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".