Contrast-Induced Nephropathy and Long-Term Adverse Events
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The relationship of contrast-induced nephropathy (CIN) to long-term adverse events (AEs) is controversial. Although an association with AEs has been previously reported, it is unclear whether CIN is causally related to these AEs. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We obtained long-term (> or =1 yr) follow-up on 294 patients who participated in a randomized, double-blind comparison of two prevention strategies for CIN (iopamidol versus iodixanol). A difference in the incidence of AEs between patients who had developed CIN and those who had not was performed using a chi(2) test and Poisson regression analysis. A similar statistical approach was used for the differences in AEs between those who received iopamidol or iodixanol. Multiple definitions of CIN were used to strengthen and validate the results and conclusions. RESULTS: The rate of long-term AEs was higher in individuals with CIN (all definitions of CIN). After adjustment for baseline comorbidities and risk factors, the adjusted incidence rate ratio for AEs was twice as high in those with CIN. Randomization to iopamidol reduced both the incidence of CIN and AEs. CONCLUSIONS: The parallel decrease in the incidence of CIN and AEs in one arm of this randomized trial supports a causal role for CIN.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".