Estimation of glomerular filtration rate in acute coronary syndromes: comparison between cockcroft-gault, modification of diet in renal disease and chronic kidney disease epidemiology collaboration
Bibliographic record
Abstract
Nov 2012 -Jan 2013) across the three centres.For all patients the median age was 68 years (58-77), BMI 28 (25-29-YZ) and 246 (69x%) were males.56 (16%) had unstable angina, 143 (41%) Non ST Segment Elevation Myocardial Infarction and 156 (43%) ST Segment Elevation Myocardial Infarction.300 (80%) patients had diagnostic angiography, revealing three vessel disease in 30%, and left main stem disease in 12%.37 (11%) patients experienced a MACE at 30 days, of which 16 (4.5%)died.Of all risk stratification tools tested, only a positive GSF score was significantly associated with adverse events (HR 1.9:1.2-2-9;p=0.03;AUC 0.70), independent of age, left ventricular ejection fraction and diagnosis of three vessel disease.Conclusion: Our interim analysis indicates that positive GSF score, but not Canadian frailty or GRACE scores provides independent prediction of short term clinical outcomes in ACS patients.Further data collection and analyses are being performed and we will present final study results of 800 patients (powered for primary endpoint) at ESC Congress in August/September 2013 at Amsterdam.
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.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".