Antibiotic Management of Lung Infections in Cystic Fibrosis. II. Nontuberculous Mycobacteria, Anaerobic Bacteria, and Fungi
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Airway infections are a key component of cystic fibrosis (CF) lung disease. Whereas the approach to common pathogens such as Pseudomonas aeruginosa is guided by a significant body of evidence, other infections often pose a considerable challenge to treating physicians. In Part I of this series on the antibiotic management of difficult lung infections, we discussed bacterial organisms including methicillin-resistant Staphylococcus aureus, gram-negative bacterial infections, and treatment of multiple bacterial pathogens. Here, we summarize the approach to infections with nontuberculous mycobacteria, anaerobic bacteria, and fungi. Nontuberculous mycobacteria can significantly impact the course of lung disease in patients with CF, but differentiation between colonization and infection is difficult clinically as coinfection with other micro-organisms is common. Treatment consists of different classes of antibiotics, varies in intensity, and is best guided by a team of specialized clinicians and microbiologists. The ability of anaerobic bacteria to contribute to CF lung disease is less clear, even though clinical relevance has been reported in individual patients. Anaerobes detected in CF sputum are often resistant to multiple drugs, and treatment has not yet been shown to positively affect patient outcome. Fungi have gained significant interest as potential CF pathogens. Although the role of Candida is largely unclear, there is mounting evidence that Scedosporium species and Aspergillus fumigatus, beyond the classical presentation of allergic bronchopulmonary aspergillosis, can be relevant in patients with CF and treatment should be considered. At present, however there remains limited information on how best to select patients who could benefit from antifungal therapy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it