Predicting 30‐day mortality in older patients with suspected infections by adding performance status to quick sequential organ failure assessment
Bibliographic record
Abstract
Background: Quick Sequential Organ Failure Assessment (qSOFA) is a simple and easy tool for identifying patients with suspected infection, who are at a high risk of poor outcome. However, its predictive performance is still insufficient. The Eastern Cooperative Oncology Group Performance Status (ECOG-PS) score, a tool to evaluate physical function, has been recently reported to be useful in predicting the prognosis of patients with pneumonia. We aimed to evaluate the added value of ECOG-PS to qSOFA in predicting 30-day mortality in older patients admitted with suspected infections. Methods: Between 2018 and 2019, we prospectively collected data from adults aged 65 years or older, admitted with suspected infection at two acute care hospitals. Predictive performance was compared between two logistic regression models: one using qSOFA score alone (qSOFA model) and the other in which ECOG-PS was added to qSOFA (extended model). Results: = 0.008). When the risk groups were categorized as follows: low (<5%), intermediate (5%-10%), and high (≥10%), 5.0% of those who died and 2.1% of those who survived were correctly reclassified by the extended model with an overall categorized net reclassification improvement of 0.03 (95% confidence interval: -0.06 to 0.30). Conclusions: Adding the ECOG-PS score could improve the performance of qSOFA in predicting mortality in older patients admitted with suspected infection.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".