Utilidade da Idade, Creatinina e Fração de Ejeção - Modificação da Dieta no Escore de Doença Renal para Prever a Sobrevivência em Pacientes com Insuficiência Cardíaca
Bibliographic record
Abstract
BACKGROUND: Central Illustration: Usefulness of Age, Creatinine and Ejection Fraction - Modification of Diet in Renal Disease Score for Predicting Survival in Patients with Heart Failure Summary of the study design and key findings. ACEF: Age, creatinine and ejection fraction, MDRD: Modified Diet in Renal Disease. While many risk models have been developed to predict prognosis in heart failure (HF), these models are rarely useful for the clinical practitioner as they include multiple variables that might be time-consuming to obtain, they are usually difficult to calculate, and they may suffer from statistical overfitting. OBJECTIVES: To investigate whether a simpler model, namely the ACEF-MDRD score, could be used for predicting one-year mortality in HF patients. METHODS: 748 cases within the SELFIE-HF registry had complete data to calculate the ACEF-MDRD score. Patients were grouped into tertiles for analyses. For all tests, a p-value <0.05 was accepted as significant. RESULTS: Significantly more patients within the ACEF-MDRD high tertile (30.0%) died within one year, as compared to other tertiles (10.8% and 16.1%, respectively, for ACEF-MDRD low and ACEF-MDRD med , p<0.001 for both comparisons). There was a stepwise decrease in one-year survival as the ACEF-MDRD score increased (log-rank p<0.001). ACEF-MDRD was an independent predictor of survival after adjusting for other variables (OR: 1.14, 95%CI:1.04 - 1.24, p=0.006). ACEF-MDRD score offered similar accuracy to the GWTG-HF score for predicting one-year mortality (p=0.14). CONCLUSIONS: ACEF-MDRD is a predictor of mortality in patients with HF, and its usefulness is comparable to similar yet more complicated models.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".