Bibliographic record
Abstract
PURPOSE OF REVIEW: Although pneumonia, either community or hospital-acquired, is the most frequent severe respiratory infection, it is an infection difficult to diagnose. At present, the diagnosis of pneumonia relies on a combination of clinical, radiologic, and microbiologic criteria. However, these criteria are far from perfect leading to uncertainty in the diagnosis, risk stratification, and choice of antibiotic therapy. Biomarkers have been used to bring additional information in this setting. RECENT FINDINGS: The aim of this review is to provide a clear overview of the current evidence for biomarkers to distinguish between patients in several clinical scenarios: to exclude pneumonia in order to withhold antibiotics, to identify the causative pathogen to target antimicrobial treatment, to identify phenotypes of inflammatory response to facilitate adjunctive treatments, to stratify the risk of severe pneumonia and provide the adequate level of care, and to monitor treatment response and de-escalate antibiotic therapy. SUMMARY: In recent years the number of new biomarkers increased markedly in different areas like pathogen identification or host response. Although far from the ideal, there are several promising areas that could represent true evolutions in the management of pneumonia, in the near future.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".