A Risk Scoring System to Identify Emergency Department Patients With Heart Failure at High Risk for Serious Adverse Events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: There are no validated guidelines to guide physicians with difficult disposition decisions for emergency department (ED) patients with heart failure (HF). The authors sought to develop a risk scoring system to identify HF patients at high risk for serious adverse events (SAEs). METHODS: This was a prospective cohort study at six large Canadian EDS that enrolled adult patients who presented with acute decompensated HF. Each patient was assessed for standardized clinical and laboratory variables as well as for SAEs defined as death, intubation, admission to a monitored unit, or relapse requiring admission. Adjusted odds ratios for predictors of SAEs were calculated by stepwise logistic regression. RESULTS: In 559 visits, 38.1% resulted in patient admission. Of 65 (11.6%) SAE cases, 31 (47.7%) occurred in patients not initially admitted. The multivariate model and resultant Ottawa Heart Failure Risk Scale consists of 10 elements, and the risk of SAEs varied from 2.8% to 89.0%, with good calibration between observed and expected probabilities. Internal validation showed the risk scores to be very accurate across 1,000 replications using the bootstrap method. A threshold of 1, 2, or 3 total scores for admission would be associated with sensitivities of 95.2, 80.6, or 64.5%, respectively, all better than current practice. CONCLUSIONS: Many HF patients are discharged home from the ED and then suffer SAEs or death. The authors have developed an accurate risk scoring system that could ultimately be used to stratify the risk of poor outcomes and to enable rational and safe disposition decisions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it