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Cystic Fibrosis Foundation Pulmonary Guidelines. Use of Cystic Fibrosis Transmembrane Conductance Regulator Modulator Therapy in Patients with Cystic Fibrosis

2018· article· en· W2784491391 on OpenAlexaff
Clement L. Ren, Rebecca L. Morgan, Christopher M. Oermann, Helaine E. Resnick, Cynthia Brady, Annette Campbell, Richard Denagel, Margaret F. Guill, Jeffrey B. Hoag, Andrew B. Lipton, Thomas Newton, Stacy Peters, Donna Beth Willey‐Courand, Edward T. Naureckas

Bibliographic record

VenueAnnals of the American Thoracic Society · 2018
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineIvacaftorCystic fibrosis transmembrane conductance regulatorGuidelineCystic fibrosisFood and drug administrationIntensive care medicineInternal medicinePhysical therapyPathologyPharmacology

Abstract

fetched live from OpenAlex

Abstract Rationale Cystic fibrosis (CF) transmembrane conductance regulator (CFTR) modulators are a new class of medications targeting the underlying defect in CF. Ivacaftor (IVA) and IVA combined with lumacaftor (LUM; IVA/LUM) have been approved by the U.S. Food and Drug Administration (FDA) for use in patients with CF. However, the FDA label for these medications encompasses patient groups that were not studied as part of the drug approval process. CF clinicians, patients, and their families have recognized a need for recommendations to guide the use of these medications. Objective Develop evidence-based guidelines for CFTR modulator therapy in patients with CF. Methods A multidisciplinary committee of CF caregivers and patient representatives was assembled. A methodologist, an epidemiologist, a medical librarian, and a biostatistician were recruited to assist with the literature search, evidence grading, and generation of recommendations. The committee developed clinical questions using the Patient-Intervention-Comparison-Outcome format. A systematic review was conducted to find relevant publications. The evidence was then evaluated using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach, and recommendations were made based on this analysis. Results For adults and children aged 6 years and older with CF due to gating mutations other than G551D or R117H, the guideline panel made a conditional recommendation for treatment with IVA. For those with the R117H mutation, the guideline panel made a conditional recommendation for treatment with IVA for 1) adults aged 18 years or older, and 2) children aged 6–17 years with a forced expiratory volume in 1 second (FEV1) less than 90% predicted. For those with the R117H mutation, the guideline panel made a conditional recommendation against treatment with IVA for 1) children aged 12–17 years with an FEV1 greater than 90% predicted, and 2) children less than 6 years of age. Among those with two copies of F508del, the guideline panel made a strong recommendation for treatment with IVA/LUM for adults and children aged 12 years and older with an FEV1 less than 90% predicted; and made a conditional recommendation for treatment with IVA/LUM for 1) adults and children aged 12 years or older with an FEV1 greater than 90% predicted, and 2) children aged 6–11 years. Conclusions Using the GRADE approach, we have made recommendations for the use of CFTR modulators in patients with CF. These recommendations will be of help to CF clinicians, patients, and their families in guiding decisions regarding use of these medications.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.080
GPT teacher head0.383
Teacher spread0.303 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations77
Published2018
Admission routes1
Has abstractyes

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