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Record W4410618347 · doi:10.14814/phy2.70387

Oscillometry with or without spirometry for methacholine testing

2025· article· en· W4410618347 on OpenAlexafffund
Cyndi Henry, Magali Boucher, Marie‐Ève Boulay, Andréanne Côté, Ynuk Bossé

Bibliographic record

VenuePhysiological Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsSpirometryMedicineMethacholineAsthmaCardiologyInternal medicineLungRespiratory disease

Abstract

fetched live from OpenAlex

Abstract Oscillometry is proposed as a complementary technique to spirometry for methacholine testing. Yet, before being prescribed in conjunction with spirometry, the extent by which the oscillometric readouts are influenced by spirometric maneuvers, especially the deep inspirations, will need to be determined. Herein, 16 very mild‐to‐mild asthmatics underwent two methacholine challenges on separate visits. On visit 1, the response was tracked by both oscillometry and spirometry, and the challenge was stopped at the provocative concentration causing a decline in forced expiratory volume in 1 s of at least 20%. The same concentration regimen was used on visit 2, but the response was tracked by oscillometry only. The results demonstrated that, except for resistance at 19 Hz, the changes in all oscillometric readouts were greater in the challenge without spirometry ( p ≤ 0.02). The maximal change in reactance at 5 Hz (X rs5 ), for example, was on average 75.8% greater in the challenge without than with spirometry ( p = 0.025). The number of doubling concentrations of methacholine that was needed to reach an equivalent change in X rs5 was also lower without concomitant spirometry ( p = 0.0078). It is concluded that the deep inspirations that are required in spirometry to monitor the response to methacholine decrease the oscillometric response.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.230
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0000.000

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 head0.067
GPT teacher head0.368
Teacher spread0.301 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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