Risk prediction of contrast-induced nephropathy by ACEF score in patients undergoing coronary catheterization
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
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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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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