4940Predicting risk at the point of care: NT-proBNP improves performance of the LACE index among patients hospitalized for Heart Failure (HF)
Bibliographic record
Abstract
Background: The Length of stay, Acuity, Comorbidities, and Emergency department visits (LACE) index can predict 30-d readmission and composite readmission/death in patients hospitalized for HF, but only with modest risk discrimination. Purpose: To assess whether admission or discharge NT-pro-BNP can improve performance of the LACE risk prediction model at the point of care (POC) among patients hospitalized for HF. Methods: This is a sub-study of the Patient-Centered Care Transitions in HF (PACT-HF) multi-center stepped wedge cluster randomized trial, which offered transitional care services to patients hospitalized for HF. We measured LACE and admission + discharge NT-proBNP at the POC. We obtained 30-d outcomes using linkages to administrative databases. We used log-binomial regression models with 30-d all-cause readmission or 30-d composite all-cause readmission/death as the outcome. We measured risk ratios (RR) with 95% confidence intervals (CI); model discrimination (C-statistic); and model calibration (Hosmer-Lemeshow test). We adjusted models for transitional care services, and performed internal validation using bootstrapping.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".