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Multiple-Breath Washout Outcome Measures in Adults with Bronchiectasis

2022· article· en· W4224242441 on OpenAlexfundno aff
Katherine O’Neill, Gokul R Lakshmipathy, Curtis Neely, Denise Cosgrove, Kathryn M. Ferguson, Rebecca H. McLeese, Adam T. Hill, Michael R. Loebinger, Mary Carroll, James D. Chalmers, Timothy Gatheral, Chris Johnson, Anthony De Soyza, John R. Hurst, Ian Bradbury, Judy Bradley

Bibliographic record

VenueAnnals of the American Thoracic Society · 2022
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsnot available
FundersMedical Research CouncilQueen's University BelfastEuropean Federation of Pharmaceutical Industries and AssociationsQueen's UniversityWellcome Trust
KeywordsBronchiectasisMedicineRepeatabilityNitrogen washoutReceiver operating characteristicCoefficient of variationVentilation (architecture)Lung volumesInternal medicineCohortLungFunctional residual capacityStatistics

Abstract

fetched live from OpenAlex

Abstract Rationale Lung clearance index (LCI) has good intravisit repeatability with better sensitivity in detecting lung disease on computed tomography scan compared with forced expiratory volume in 1 second (FEV1) in adults with bronchiectasis. Alternative multiple-breath washout parameters have not been systematically studied in bronchiectasis. Objectives To determine the validity, repeatability, sensitivity, specificity, and feasibility of standard LCI (LCI2.5), shortened LCI (LCI5.0), ventilation heterogeneity arising within proximal conducting airways (ScondVT), and ventilation heterogeneity arising within the acinar airways (SacinVT) in a cross-sectional observational cohort of adults with bronchiectasis. Methods Cross-sectional multiple-breath nitrogen washout data (Exhalyzer D; Eco Medics AG) from 132 patients with bronchiectasis across five United Kingdom centers (BronchUK Clinimetrics study) and 88 healthy control subjects were analyzed. Results Within-test repeatability (mean coefficient of variation) was <5% for both LCI2.5 and LCI5.0 in patients with bronchiectasis, and there was no difference in mean coefficient of variation for LCI2.5 and LCI5.0 in patients with bronchiectasis compared with healthy volunteers. Moderate-strength correlations were seen between FEV1 and LCI2.5 (r = −0.54), LCI5.0 (r = −0.53), ScondVT (r = −0.35), and SacinVT (r = −0.38) z-scores. The proportion of subjects with abnormal multiple-breath washout (z-score > 2) but in normal FEV1 (z-score < −2) was 42% (LCI2.5) and 36% (LCI5.0). Overall results from the receiver operating characteristic curve analysis indicated that LCI2.5 had the greatest combined sensitivity and specificity to discriminate between bronchiectasis and control subjects, followed by LCI5.0, FEV1, and ScondVT z-scores. There was a 57% time saving with LCI5.0. Conclusions LCI2.5 and LCI5.0 had good within-test repeatability and superior sensitivity compared with spirometry measures in differentiating between health and bronchiectasis disease. LCI5.0 is quicker and more feasible than LCI2.5. Clinical trial registered with www.clinicaltrials.gov (NCT 02468271).

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)0.002
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.086
GPT teacher head0.410
Teacher spread0.324 · 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".

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Citations12
Published2022
Admission routes1
Has abstractyes

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