P6544All you need is LE: utility of an abbreviated LACE score in predicting 30-day outcomes among patients hospitalized for Heart Failure (HF)
Bibliographic record
Abstract
Background: Length of stay, Acuity, Comorbidities, and Emergency department (ED) visits (LACE) can predict 30-day clinical outcomes when measured at the point of care (POC) in patients hospitalized for HF. However Comorbidities (C) are difficult to score and most patients likely present with high Acuity (A). Purpose: To determine whether an abbreviated LACE score, based only on Length of stay and number of ED visits in the prior 6 months (LE), can reliably predict 30-day readmission and 30-day composite readmission/death when used at the POC in patients hospitalized for HF. Methods: This is a sub-study of the Patient-Centered Care Transitions in HF (PACT-HF) pragmatic stepped wedge cluster randomized trial, which implemented transitional care services for HF across 10 hospitals in Canada. We included patients hospitalized for HF and discharged alive. We measured LACE and LE at the POC, and obtained 30-day clinical outcomes via linkages to administrative databases. We used log-binomial regression models with either LACE or LE as the predictor and either 30-day all-cause readmission or 30-day composite all-cause readmission/death as the outcome. We assessed risk with risk ratios (RR) and 95% confidence intervals (CI); model discrimination with C-statistic; and model calibration with a Hosmer-Lemeshow test. We adjusted all models for PACT-HF services received, and internally validated the models 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".