Treatment of early Pseudomonas aeruginosa infection in patients with cystic fibrosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: While Pseudomonas aeruginosa continues to be the major pathogen in cystic fibrosis lung disease, there is increasing evidence that antibiotic therapy initiated early after the onset of infection is an effective strategy to postpone chronic P. aeruginosa infection. There are also studies showing that early treatment can eradicate the organism in the majority of cases. RECENT FINDINGS: While comparative data for different treatment strategies are currently lacking, inhaled antibiotic therapy alone or in combination with ciprofloxacin has been shown to be efficacious. So far eradication of P. aeruginosa has not been associated with an increased risk of airway infection with other pathogens, but these studies have been performed in small cohorts. Additional information is also needed to clarify the optimal form and duration of therapy. Currently, there are two large studies ongoing to resolve some of these issues. SUMMARY: There is sufficient evidence that early antibiotic therapy against P. aeruginosa can clear the bacteria from the respiratory tract. While the negative effects of chronic P. aeruginosa infection are well documented, long-term benefits of this intervention on lung function is lacking. Observational studies suggest, however, that early intervention therapy is both beneficial and cost effective for cystic fibrosis patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".