ERS/ESICM/ESCMID/ALAT guidelines for the management of severe community-acquired pneumonia
Bibliographic record
Abstract
BACKGROUND: Severe community-acquired pneumonia (sCAP) is associated with high morbidity and mortality, and while European and non-European guidelines are available for community-acquired pneumonia, there are no specific guidelines for sCAP. MATERIALS AND METHODOLOGY: The European Respiratory Society (ERS), European Society of Intensive Care Medicine (ESICM), European Society of Clinical Microbiology and Infectious Diseases (ESCMID) and Latin American Thoracic Association (ALAT) launched a task force to develop the first international guidelines for sCAP. The panel comprised a total of 18 European and four non-European experts, as well as two methodologists. Eight clinical questions for sCAP diagnosis and treatment were chosen to be addressed. Systematic literature searches were performed in several databases. Meta-analyses were performed for evidence synthesis, whenever possible. The quality of evidence was assessed with GRADE (Grading of Recommendations, Assessment, Development and Evaluation). Evidence to Decision frameworks were used to decide on the direction and strength of recommendations. RESULTS: Recommendations issued were related to diagnosis, antibiotics, organ support, biomarkers and co-adjuvant therapy. After considering the confidence in effect estimates, the importance of outcomes studied, desirable and undesirable consequences of treatment, cost, feasibility, acceptability of the intervention and implications to health equity, recommendations were made for or against specific treatment interventions. CONCLUSIONS: In these international guidelines, ERS, ESICM, ESCMID and ALAT provide evidence-based clinical practice recommendations for diagnosis, empirical treatment and antibiotic therapy for sCAP, following the GRADE approach. Furthermore, current knowledge gaps have been highlighted and recommendations for future research have been made.
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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 | 0.029 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".