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Record W4281690527 · doi:10.1183/20734735.0005-2022

Management of pregnancy in cystic fibrosis

2022· review· en· W4281690527 on OpenAlexaff
Kristina Montemayor, Elizabeth Tullis, Raksha Jain, Jennifer L. Taylor‐Cousar

Bibliographic record

VenueBreathe · 2022
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsCystic fibrosisPregnancyMedicineComputer scienceObstetricsInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.909
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.052
GPT teacher head0.372
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations18
Published2022
Admission routes1
Has abstractyes

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