Management of pregnancy in cystic fibrosis
Bibliographic record
Abstract
With recent therapeutic advances in care, people with cystic fibrosis (CF) are living longer and healthier lives. Development of the cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapies has led to improved function of the CFTR protein resulting in improved lung function, decreased rates of pulmonary exacerbations and improved nutritional status for the majority of people with CF. Given improved quality and quantity of life, more people with CF are considering becoming pregnant than ever before. Since the first reported pregnancy in a woman with CF in 1960, the management of pregnancy in CF has been of increased interest and is an active field of research. In this review, we aim to discuss the management of pregnancy in CF. We discuss the optimisation of preconception health, management of maintenance CF therapies, and use of CFTR modulators during pregnancy and lactation. We also describe the management of pulmonary exacerbations during pregnancy as well as the unique management of pregnancy in a post-transplant patient with CF. Educational aims: To describe considerations for optimisation of preconception health.To describe the management of chronic CF therapies including CFTR modulators during pregnancy and lactation.To describe treatment of an acute pulmonary exacerbation during pregnancy.To describe the management of pregnancy in individuals with CF following organ transplantation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".