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Record W3201704325 · doi:10.1183/20734735.0016-2021

Multiple breath washout: measuring early manifestations of lung pathology

2021· article· en· W3201704325 on OpenAlexaff
Sanja Stanojevic, Cole Bowerman, Paul D. Robinson

Bibliographic record

VenueBreathe · 2021
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWashoutMedicinePathologyLungComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

The multiple breath washout (MBW) test measures the efficiency of gas mixing in the lungs and has gained significant interest over the past 20 years. MBW outcomes detect early lung function impairment and peripheral airway pathology, through its main outcome measure lung clearance index (LCI). LCI measures the number of lung turnovers required to washout an inert tracer gas. MBW is performed during normal (tidal) breathing, making it particularly suitable for young children or those who have trouble performing forced manoeuvres. Additionally, research in chronic respiratory disease populations has shown that MBW can detect acute clinically relevant changes before conventional lung function tests, such as spirometry, thus enabling early intervention. The development of technical standards for MBW and commercial devices have allowed MBW to be implemented in clinical research and potentially routine clinical practice. Although studies have summarised clinimetric properties of MBW indices, additional research is required to establish the clinical utility of MBW and, if possible, shorten testing time. Sensitive, feasible measures of early lung function decline will play an important role in early intervention for people living with respiratory diseases. EDUCATIONAL AIM: To describe the multiple breath washout test, its applications to lung pathology and respiratory disease, as well as directions for future research.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.026
GPT teacher head0.303
Teacher spread0.277 · 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.

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

Citations36
Published2021
Admission routes1
Has abstractyes

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