Agreement and comparative accuracy of instability criteria at discharge for predicting adverse events in patients with community-acquired pneumonia
Bibliographic record
Abstract
Objective Five definitions of clinical instability have been published to assess the appropriateness and safety of discharging patients hospitalised for pneumonia. This study aimed to quantify the level of agreement between these definitions and estimate their discriminatory accuracy in predicting post-discharge adverse events. Study design and setting We conducted a retrospective cohort study involving 1038 adult patients discharged alive following hospitalisation for pneumonia. Results The prevalence of unstable criteria within 24 hours before discharge was 4.5% for temperature >37.8°C, 13.8% for heart rate >100/min, 1.0% for respiratory rate >24/min, 2.6% for systolic blood pressure <90 mm Hg, 3.3% for oxygen saturation <90%, 5.4% for inability to maintain oral intake and 6.4% for altered mental status. The percentage of patients classified as unstable at discharge ranged 12.8%–41.0% across different definitions (Fleiss Kappa coefficient, 0.47; 95% CI 0.44 to 0.50). Overall, 140 (13.5 %) patients experienced adverse events within 30 days of discharge, including 108 unplanned readmissions (10.4%) and 32 deaths (3.1%). Clinical instability was associated with a 1.3-fold to 2.0-fold increase in the odds of postdischarge adverse events, depending on the definition, with c-statistics ranging 0.54–0.59 (p=0.31). Conclusion Clinical instability was associated with higher odds of 30-day postdischarge adverse events according to all but one of the published definitions. This study supports the validity of definitions that combine vital signs, mental status and the ability to maintain oral intake within 24 hours prior to discharge to identify patients at a higher risk of postdischarge adverse events.
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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 | 0.025 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".