Optimizing Antibacterial Therapy for Community-Acquired Respiratory Tract Infections in Children in an Era of Bacterial Resistance
Bibliographic record
Abstract
The spread of antibacterial resistance in bacteria that commonly cause childhood community-acquired respiratory tract infections (RTIs), such as acute otitis media, community-acquired pneumonia, and acute pharyngitis, is a major healthcare problem. One of the foremost concerns is the rapid increase in penicillin, macrolide, and multidrug resistance in Streptococcus pneumoniae. There is also a rising prevalence of macrolide resistance in Streptococcus pyogenes in pockets of the United States, and beta-lactamase production in Haemophilus influenzae is widespread. Although data are limited, some evidence suggests that resistance to antibacterials can impair bacteriologic and clinical outcomes in childhood RTIs. Optimizing antibacterial use is important both in the care of individual patients and within strategies to address the wider problem of antibacterial resistance. This involves encouraging judicious antibacterial use (i.e., reducing overuse for viral infection and prophylaxis), and preventing misuse through the wrong choice, dosage, and duration of therapy. Given that initial therapy is usually empiric, antibacterials used to treat community-acquired RTIs in children should ideally have the following properties: an optimal targeted spectrum of activity; high clinical and bacteriologic efficacy against respiratory pathogens, including resistant strains; simple, short-course therapy; and good tolerability and palatability. New antibacterials will continue to have a role in the treatment of RTIs in children, especially where resistance compromises existing therapies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".