The Use of Polymerase Chain Reaction Amplification for the Detection of Viruses and Bacteria in Severe Community-Acquired Pneumonia
Bibliographic record
Abstract
BACKGROUND: Pathogens are often not identified in severe community-acquired pneumonia (CAP), and the few studies using polymerase chain reaction (PCR) techniques for virus detection are from temperate countries. OBJECTIVE: This study assesses if PCR amplification improves virus and bacteria detection, and if viral infection contributes to mortality in severe CAP in a tropical setting, where respiratory pathogens have less well-defined seasonality. METHODS: In this cohort study of patients with severe CAP in an intensive care unit, endotracheal aspirates for intubated patients and nasopharyngeal swabs for non-intubated patients were sent for PCR amplification for respiratory viruses. Blood, endotracheal aspirates for intubated patients, and sputum for non-intubated patients were analysed using a multiplex PCR system for bacteria. RESULTS: Out of 100 patients, using predominantly cultures, bacteria were identified in 42 patients; PCR amplification increased this number to 55 patients. PCR amplification identified viruses in 32 patients. In total, only bacteria, only viruses, and both bacteria and viruses were found in 37, 14, and 18 patients, respectively. The commonest viruses were influenza A H1N1/2009 and rhinovirus; the commonest bacterium was Streptococcus pneumoniae. Hospital mortality rates for patients with no pathogens, bacterial infection, viral infection, and bacterial-viral co-infection were 16.1, 24.3, 0, and 5.6%, respectively (p = 0.10). On multivariable analysis, virus detection was associated with lower mortality (adjusted odds ratio 0.12, 95% confidence interval 0.2-0.99; p = 0.049). CONCLUSIONS: Viruses and bacteria were detected in 7 of 10 patients with severe CAP with the aid of PCR amplification. Viral infection appears to be independently associated with lower mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".