Challenges in managing Pseudomonas aeruginosa in non-cystic fibrosis bronchiectasis
Bibliographic record
Abstract
BACKGROUND: An Expert Forum was held at the 2014 European Respiratory Society International Congress to address issues involved in the management of Pseudomonas aeruginosa infection in patients with non-cystic fibrosis bronchiectasis (NCFB). Multiple studies have found that chronic P. aeruginosa infection is associated with more severe disease and higher morbidity and mortality. OVERVIEW: Participants discussed appropriate management of P. aeruginosa infection at three stages: 1) first isolation, including eradication protocols; 2) during exacerbations; and 3) during chronic infection, including long-term antibiotic therapy to reduce the severity of symptoms and frequency of exacerbations. Topics covered included frequency of sputum cultures, antibiotic treatment at first isolation and for exacerbations, optimal use of inhaled antibiotics, indications for long-term therapy, and treatment regimens that may reduce the frequency or severity of symptoms. Electronic polling and roundtable discussions followed by expert insights were used to address these topics. Significant diversity in management practices was reported among different countries and centres, and in many cases clinical management was at variance with published guidelines. CONCLUSIONS: This Expert Forum identified standardised terminology, clinician training, additional research into management strategies, and the development of new drugs as areas requiring improvement for the optimal management of P. aeruginosa in NCFB.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".