Contrast-Induced Nephropathy and Long-Term Adverse Events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it