Abstract 13934: Low Estimated Protein Intake is Associated With Poor Prognosis in Patients With Acute Heart Failure
Bibliographic record
Abstract
Introduction: Although a higher protein intake has been related with lower mortality rates in general population, the association between protein intake and nutritional status/mortality in patients with acute heart failure has yet to be clarified. Methods and Results: We retrospectively analyzed 694 patients who were admitted due to acute heart failure in our hospital (mean age, 75±13 years; male 60%). The estimated protein intake was defined as a validated formula: [13.9 + 0.907*body mass index (kg/m 2 ) + 0.0305*urinary urea nitrogen level (mg/dL)] using spot urine samples on admission. All patients were divided into three groups according to the estimated protein intake: low (≤43.6 g/day, n=232), middle (43.7 to 51.5 g/day, n=231), and high (≥51.6 g/day, n=231) group. The primary outcome of this study was regarded as all-cause mortality. Patients with low protein intake were older and had lower albumin compared with other two groups. A lower protein intake was associated with worse nutritional status evaluated using Geriatric Nutritional Risk Index (P<0.001). Kaplan-Meier analysis revealed that the low protein intake group was significantly associated with higher incidence of the primary outcome. Compared to the high protein intake group, Cox proportional hazard analysis demonstrated the low protein intake group was independently associated with all-cause mortality (hazard ratio, 1.80; 95% confidence interval, 1.07-3.02; P=0.026) even after adjustment for confounding factors. Conclusions: Low protein intake was associated with poor nutritional status and all-cause mortality in patients with acute heart failure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".