Prognostic value of Pneumonia Severity Index, CURB-65, CRB-65, and procalcitonin in community-acquired pneumonia in Singapore
Bibliographic record
Abstract
Objective: The purpose of this study was to evaluate the performance of three severity scoring tools and procalcitonin (PCT) in severity stratification and mortality prediction among patients with community-acquired pneumonia (CAP) in Singapore. Methods: The method used was a retrospective observational study of all the consecutive patients with CAP admitted through the emergency department of Singapore General Hospital between 2012–2013. Results: Among 1902 study subjects, the overall 30-day mortality was 15.7%. The mortality rates for Pneumonia Severity Index (PSI) class I–III were 0, 0, and 3.7%, which were comparable to the original published data. CURB-65 and CRB-65 had higher mortality rates in all severity levels. In three levels of risk stratification, the low risk group of PSI (class I–III) included 42.6% of the patients with mortality rate of 1.9%, whereas the low risk group defined by CURB-65 (score 0–1) and CRB-65 (score 0) included 52.0% and 24.4% of the patients with higher mortality rates (7.3% and 4.5% respectively). PSI was the most sensitive in mortality prediction with area under receiver operating characteristic (ROC) curve of 0.82, higher than CURB-65 (0.71), CRB-65 (0.67), and PCT (0.63) ( p<0.001). The initial level of PCT was higher in non-survivors and intensive care unit (ICU)-admitted patients compared to survivors (0.91 vs 0.36 ng/ml, p<0.001) and non-ICU patients (3.70 vs 0.38 ng/ml, p<0.001). Incorporating PCT did not improve the discriminatory power of the scoring tools for mortality prediction. Conclusions: PSI was a reliable tool for severity stratification and morality prediction among the patients with CAP in Singapore.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".