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Record W4385330950 · doi:10.1183/13993003.01519-2022

European Respiratory Society/American Thoracic Society technical statement: standardisation of the measurement of lung volumes, 2023 update

2023· article· en· W4385330950 on OpenAlexaff
Nirav R. Bhakta, Aisling McGowan, Kathryn Ramsey, Brigitte M. Borg, Jana Kivastik, Shandra L. Knight, Karl Sylvester, Felip Burgos, Erik R. Swenson, Kevin F. McCarthy, Brendan Cooper, Francisco García‐Río, Gwen Skloot, Meredith C. McCormack, Carl Mottram, Charles G. Irvin, Irene Steenbruggen, Allan L. Coates, David A. Kaminsky

Bibliographic record

VenueEuropean Respiratory Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicinePulmonologistsGrading (engineering)SpirometryMedical physicsTask forceIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

This document updates the 2005 European Respiratory Society (ERS) and American Thoracic Society (ATS) technical standard for the measurement of lung volumes. The 2005 document integrated the recommendations of an ATS/ERS task force with those from an earlier National Heart, Lung, and Blood Institute workshop that led to the publication of background papers between 1995 and 1999 and a consensus workshop report with more in-depth descriptions and discussion. Advancements in hardware and software, new research and emerging approaches have necessitated an update to the 2005 technical standard to guide laboratory directors, physiologists, operators, pulmonologists and manufacturers. Key updates include standardisation of linked spirometry, new equipment quality control and validation recommendations, generalisation of the multiple breath washout concept beyond nitrogen, a new acceptability and grading system with addition of example tracings, and a brief review of imaging and other new techniques to measure lung volumes. Future directions and key research questions are also noted.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.053
GPT teacher head0.343
Teacher spread0.290 · 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.

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

Citations140
Published2023
Admission routes1
Has abstractyes

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