Ultra-low-contrast angiography in patients with advanced chronic kidney disease and previous coronary artery bypass surgery
Bibliographic record
Abstract
OBJECTIVE: We sought to describe a technique for ultra-low-contrast angiography (ULCA) in patients with advanced chronic kidney disease (CKD) and previous coronary artery bypass surgery (CABG). BACKGROUND: Patients with advanced CKD and previous CABG are at high risk of developing contrast-induced nephropathy (CIN) because of the additional contrast often required to identify bypass grafts. Apart from hydration, reduced contrast administration is the only established method to minimize the risk of CIN. PATIENTS AND METHODS: Ten patients underwent ULCA, whereby an intracoronary injection of saline and coronary guidewires were used instead of test injections of contrast for engagement of bypass grafts with catheters. Estimated glomerular filtration rate (eGFR) before and 30 days following angiography were recorded as was the need for renal replacement therapy 1 year after the procedure. RESULTS: All patients completed a diagnostic angiogram without complications. The median volume of contrast delivered was 13.5 ml (interquartile range: 10.5-17.8). The median eGFR was 18.3 ml/min/1.73 m (interquartile range: 16.5-28.2). There was no statistically significant difference in eGFR before the procedure and 30 days after the procedure (P=0.79). No patient required dialysis 30 days after the procedure. Two patients required initiation of dialysis at 1 year after the procedure. CONCLUSION: In patients with advanced CKD and previous CABG, ULCA may be performed with high procedural success and without complications, minimizing the risk of CIN in these high-risk patients.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".