Canadian Association of Radiologists Consensus Guidelines for the Prevention of Contrast-Induced Nephropathy: Update 2012
Bibliographic record
Abstract
Purpose Contrast-induced acute kidney injury or contrast-induced nephropathy (CIN) is a significant complication of intravascular contrast medium (CM). These guidelines are intended as a practical approach to risk stratification and prevention. The major risk factor that predicts CIN is pre-existing chronic kidney disease. Methods Members of the committee represent radiologists and nephrologists across Canada. The previous guidelines were reviewed, and an in-depth up-to-date literature review was carried out. Results A serum creatinine level (SCr) should be obtained, and an estimated glomerular filtration rate (eGFR) should be calculated within 6 months in the outpatient who is stable and within 1 week for inpatients and patients who are not stable. Patients with an eGFR of ≥ 60 mL/min have an extremely low risk of CIN. The risk of CIN after intra-arterial CM administration appears be at least twice that after intravenous administration. Fluid volume loading remains the single most important measure, and hydration regimens that use sodium bicarbonate or normal saline solution should be considered for all patients with GFR < 60 mL/min who receive intra-arterial contrast and when GFR < 45 mL/min in patients who receive intravenous contrast. Patients are most at risk for CIN when eGFR < 30 mL/min. Additional preventative measures include the following: avoid dehydration, avoid CM when appropriate, minimize CM volume and frequency, avoid high osmolar CM, and discontinue nephrotoxic medications 48 hours before administration of CM.
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".