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Oscillatory Mechanics in Asthma: Emphasis on Airway Variability and Heterogeneity

2015· review· en· W2494756298 on OpenAlexaff
Swati A. Bhatawadekar, Paul Hernandez, Geoffrey N. Maksym

Bibliographic record

VenueCritical Reviews in Biomedical Engineering · 2015
Typereview
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsDalhousie UniversityQueen Elizabeth II Health Sciences CentreToronto Rehabilitation InstituteUniversity Health Network
Fundersnot available
KeywordsSpirometryAsthmaMedicineRespiratory physiologyAirwayAirway obstructionIntensive care medicineAirway hyperresponsivenessLung functionBreathingRespiratory systemLungInternal medicineSurgeryAnesthesia

Abstract

fetched live from OpenAlex

Spirometry is one of the most widely used tests in the assessment and monitoring of asthma. However, spirometry cannot be performed in very young children and some adult patients, and is poorly sensitive to small airways, which are primarily involved in the pathophysiology of asthma. The forced oscillation technique (FOT) has emerged as a powerful alternative technique that instead characterizes respiratory mechanics during normal breathing with no forced maneuver. In this review we highlight the current state of the art of the FOT and its utility in the assessment of lung function in asthma. First we briefly discuss the clinical features and characteristics of asthma. This is followed by a discussion of the assessment of airway obstruction and airway hyperresponsiveness using spirometry. We then review the basics of FOT and its application in respiratory diseases. FOT data are particularly amenable to modeling as an aide to physiological interpretation, and we review several common approaches. This is followed by an in-depth discussion of the assessment of airway variability and heterogeneity using FOT in asthma. Finally, we speculate on the potential clinical utility of FOT in asthma.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.045
GPT teacher head0.333
Teacher spread0.289 · 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 designOther design
Domainnot available
GenreReview

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

Citations7
Published2015
Admission routes1
Has abstractyes

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