Risk prediction of contrast-induced nephropathy by ACEF score in patients undergoing coronary catheterization
Bibliographic record
Abstract
AIMS: To explore the ability of the ACEF score to predict the incidence of contrast-induced nephropathy (CIN) in patients undergoing coronary angiography with or without percutaneous coronary intervention. METHODS: A total of 706 patients undergoing coronary angiography ± percutaneous coronary intervention (PCI) between March 2011 and October 2011 were analyzed. CIN using different definitions was termed as CINnarrow (rise in serum creatinine ≥0.5 mg/dl) and CINbroad (rise in serum creatinine ≥0.5 mg/dl and/or ≥25% increase in baseline serum creatinine). RESULTS: The mean ACEF score was 1.5 ± 0.6. Overall incidences of CINnarrow and CINbroad were 5.5% and 13.6%, respectively. There was a significant gradient in the incidence of CINnarrow (2.9%, 3.9%, 10.6% in the I, II, and III tertiles, respectively, P < 0.001) and CINbroad (9.1%, 14.2%, 17.9% in the I, II, and III tertiles, respectively, P = 0.021) across increasing ACEF tertiles. The ACEF score was independently associated with the risk of CINnarrow (adjusted odds ratio [OR] 1.6, 95% confidence interval [CI] 1.0-2.7; P = 0.047). Discrimination was more satisfactory when using the ACEF as a predictor of CINnarrow (c-statistic 0.71, 95% 0.63-0.79). CONCLUSION: The ACEF score is an independent and potentially useful predictor of CIN defined as rise in serum creatinine ≥0.5 mg/dl.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".