Age, glomerular filtration rate, ejection fraction, and the AGEF score predict contrast‐induced nephropathy in patients with acute myocardial infarction undergoing primary percutaneous coronary intervention
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: In patients undergoing primary percutaneous coronary interventions (PCI) for ST-segment elevation myocardial infarction (STEMI), the occurrence of Contrast-Induced Nephropathy (CIN) has a pronounced impact both on morbidity and mortality. We investigated the variables associated with CIN development in 481 consecutive patients with STEMI undergoing primary PCI and evaluated the predictive value of a 3-variable clinical risk score (the AGEF score) based on age, left ventricular ejection fraction (EF), and estimated glomerular filtration rate (eGFR). METHODS: CIN was defined as an absolute increase in serum creatinine ≥0.5 mg/dL or an increase ≥25% from baseline within 72 hr. AGEF score was calculated by adding 1 point to the Age/EF(%) ratio if the eGFR was <60 mL/min per 1.73 m(2) . RESULTS: Overall, the incidence of CIN was 5.2%. In-hospital mortality was higher in patients with CIN than in those without (16% Vs 1.3%, P = 0.001). At multivariate analysis age (OR 1.06, P = 0.042), eGFR (OR 0.95, P = 0.001), EF (OR 0.94, P = 0.007) and post-procedural TIMI flow grade (OR 0.43, P = 0.045) were independent predictors of CIN. AGEF score was an accurate (OR 5.19, P < 0.001, AUC 0.88) and calibrated (Hosmer-Lemeshow χ(2) = 10.25, P = 0.25) predictor of CIN. CONCLUSIONS: Advanced age, depressed EF, and reduced eGFR are independent predictors of CIN development after primary PCI for STEMI. The preprocedural individual patient risk can be clinically assessed with the calculation of the AGEF score, which is based on such readily available parameters.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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