Bibliographic record
Abstract
Community-acquired pneumonia (CAP) remains a major cause of illness and death worldwide, particularly among the elderly and individuals with underlying conditions. Streptococcus pneumoniae is the most common bacterial cause of CAP, but viruses such as influenza, RSV, and COVID-19 also play a significant role. Within the European PNEUMO study, we investigated how frequently pneumococcal pneumonia occurs, which S. pneumoniae serotypes are circulating, and what their disease burden is. During the COVID-19 pandemic, the number of bacterial pneumonias initially dropped sharply but later increased again. Serotypes 3, 11A, and 8 were found most frequently, some of which are not yet included in current vaccines. This highlights the need for broader vaccines and ongoing surveillance. A second theme of the thesis focuses on the use of biomarkers to improve pneumonia treatment. The biomarker procalcitonin (PCT) proved valuable in reducing unnecessary antibiotic use among COVID-19 patients without negatively affecting outcomes. However, in patients with influenza or RSV infection, the added value of PCT was less pronounced. Other biomarkers, such as suPAR, showed limited usefulness in predicting disease progression. Finally, we evaluated the applicability of diagnostic decision rules for excluding pulmonary embolism in COVID-19 patients, which showed that existing methods should be applied with caution. These findings contribute to a better understanding of pneumonia, more rational use of antibiotics, and improved strategies for future vaccination and treatment.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".