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Record W4408344605 · doi:10.1016/j.jcf.2025.03.004

Vitality is associated with systemic inflammation in cystic fibrosis adults on elexacaftor/tezacaftor/ivacaftor

2025· article· en· W4408344605 on OpenAlexafffundabout
J. Gravelle, Sameer Desai, Kang Dong, Bradley S. Quon

Bibliographic record

VenueJournal of Cystic Fibrosis · 2025
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of British Columbia
FundersCystic Fibrosis Canada
KeywordsIvacaftorCystic fibrosisMedicineVitalityInflammationSystemic inflammationProgeriaInternal medicineCystic fibrosis transmembrane conductance regulatorGenetics

Abstract

fetched live from OpenAlex

Fatigue is common among adults with cystic fibrosis (awCF) and may be associated with systemic inflammation. This study examines systemic inflammation, measured by C-reactive protein (CRP), and fatigue, assessed using the Cystic Fibrosis Questionnaire-Revised (CFQ-R) vitality domain, in individuals initiating elexacaftor/tezacaftor/ivacaftor (ETI) therapy. In a cohort of 61 awCF from St. Paul's Hospital, Vancouver, CRP and vitality were measured at baseline and at 1, 3, 6, and 12 months post-ETI initiation. We observed reductions in CRP and increases in vitality over the 12-month period. Linear mixed-effects models were used to examine the relationship between CRP and vitality adjusted for age, sex, BMI, and lung function. Our findings demonstrated a significant, independent inverse association between CRP and vitality. These results highlight the potential role of systemic inflammation in influencing vitality in awCF undergoing ETI therapy. Further research incorporating additional inflammatory markers and psychosocial variables is warranted to deepen our understanding of fatigue mechanisms in this population.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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

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 head0.009
GPT teacher head0.282
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Cystic FibrosisSame topicCystic Fibrosis Research AdvancesFrench-language works237,207