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Record W1959983245 · doi:10.1111/resp.12470

Effect of equipment dead space on multiple breath washout measures

2015· article· en· W1959983245 on OpenAlexaff
Anouk Benseler, Sanja Stanojevic, Renée Jensen, Per Gustafsson, Félix Ratjen

Bibliographic record

VenueRespirology · 2015
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineDead spaceRespiratory systemInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVE: Multiple breath inert gas washout (MBW) systems are designed to minimize equipment dead space volume (Vd). Animal and infant studies have demonstrated the impact of increased Vd on MBW measurements. In this study, we investigate the effect of Vd of a nitrogen (N2 ) MBW system on MBW measurements in preschool children. METHODS: N2 MBW measurements were performed in healthy adults under standard conditions; Vd was added to match the relationship between Vd and lung volumes observed in preschool children. Subsequently, subjects were measured on a sulfur hexafluoride (SF6 ) MBW system under standard conditions and with Vd added to match that of the N2 MBW system. Healthy preschool children and children with cystic fibrosis were tested on both the N2 MBW and SF6 MBW in random order on the same day. A correction equation was derived based on the adult experiments and tested on the preschool data. RESULTS: Increasing the Vd of the N2 MBW system resulted in a higher lung clearance index (LCI). A strong non-linear relationship between N2 LCI and the Vd/tidal volume was observed. When the Vd was equivalent between systems, LCI measured by the SF6 MBW system was similar to that measured by the N2 MBW. LCI was higher on the N2 MBW than the SF6 MBW in preschool children. Correcting for the equipment Vd of the N2 MBW resulted in better agreement. CONCLUSIONS: Equipment Vd affects LCI measurements especially in young children where Vd is large relative to lung volumes.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
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 head0.037
GPT teacher head0.360
Teacher spread0.323 · 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 designBench or experimental
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

Citations30
Published2015
Admission routes1
Has abstractyes

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