The Prognostic Importance of Changes in Renal Function during Treatment for Acute Heart Failure Depends on Admission Renal Function
Bibliographic record
Abstract
BACKGROUND: Worsening and improving renal function during acute heart failure have been associated with adverse outcomes but few studies have considered the admission level of renal function upon which these changes are superimposed. OBJECTIVES: The objective of this study was to evaluate definitions that incorporate both admission renal function and change in renal function. METHODS: 696 patients with acute heart failure with calculable eGFR were classified by admission renal function (Reduced [R, eGFR<45 ml/min] or Preserved [P, eGFR≥45 ml/min]) and change over hospital admission (worsening [WRF]: eGFR ≥20% decline; stable [SRF]; and improving [IRF]: eGFR ≥20% increase). The primary outcome was all-cause mortality. The prevalence of Pres and Red renal function was 47.8% and 52.2%. The frequency of R-WRF, R-SRF, and R-IRF was 11.4%, 28.7%, and 12.1%, respectively; the incidence of P-WRF, P-SRF, and P-IRF was 5.7%, 35.3%, and 6.8%, respectively. Survival was shorter for patients with R-WRF compared to R-IRF (median survival times 13.9 months (95%CI 7.7-24.9) and 32.5 months (95%CI 18.8-56.1), respectively), resulting in an acceleration factor of 2.3 (p = 0.016). Thus, an increase compared with a decrease in renal function was associated with greater than two times longer survival among patients with Reduced renal function.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".