The burden of community-acquired bacterial pneumonia in the era of antibiotic resistance
Bibliographic record
Abstract
INTRODUCTION: Community-acquired pneumonia (CAP) is a significant global health problem and leading cause of death and hospitalization in both the US and abroad. Increasing macrolide resistance among Streptococcus pneumoniae and other pathogens results in a greater disease burden, along with changing demographics and a higher preponderance of comorbid conditions. Areas covered: This review summarizes current data on the clinical and economic burden of CAP, with particular focus on community-acquired bacterial pneumonia (CABP). Incidence, morbidity and mortality, and healthcare costs for the US and other regions of the world are among the topics covered. Major factors that are believed to be contributing to the increased impact of CABP, including antimicrobial resistance, the aging population, and the incidence of comorbidities are discussed, as well as unmet needs in current CABP management. Expert commentary: The clinical and economic burden of CABP is staggering, far-reaching, and expected to increase in the future as new antibiotic resistance mechanisms emerge and the world's population ages. Important measures must be initiated to stabilize and potentially decrease this burden. Urgent needs in CABP management include the development of new antimicrobials, adjuvant therapies, and rapid diagnostics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".