Assessing Risk and Preventing 30-Day Readmissions in Decompensated Heart Failure: Opportunity to Intervene?
Bibliographic record
Abstract
Heart failure (HF) patients are at high risk of hospital readmission, which contributes to substantial health care costs. There is great interest in strategies to reduce rehospitalization for HF. However, many readmissions occur within 30 days of initial hospital discharge, presenting a challenge for interventions to be instituted in a short time frame. Potential strategies to reduce readmissions for HF can be classified into three different forms. First, patients who are at high risk of readmission can be identified even before their initial index hospital discharge. Second, ambulatory remote monitoring strategies may be instituted to identify early warning signs before acute decompensation of HF occurs. Finally, strategies may be employed in the emergency department to identify low-risk patients who may not need hospital readmission. If symptoms improve with initial therapy, low-risk patients could be referred to specialized, rapid outpatient follow-up care where investigations and therapy can occur in an outpatient setting.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".