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Phonospirometry for noninvasive measurement of ventilation: methodology and preliminary results

2002· article· en· W2138886600 on OpenAlexaff
Chengli Que, Christof Kolmaga, Louis‐Gilles Durand, Suzanne M. Kelly, Peter T. Macklem

Bibliographic record

VenueJournal of Applied Physiology · 2002
Typearticle
Languageen
FieldMedicine
TopicPhonocardiography and Auscultation Techniques
Canadian institutionsUniversité de MontréalMontreal Clinical Research InstituteRoyal Victoria Hospital
Fundersnot available
KeywordsMouthpieceTidal volumeSupine positionVentilation (architecture)MedicineRespiratory minute volumeRespiratory systemAnesthesiaAirwayAnatomyPhysicsMeteorology

Abstract

fetched live from OpenAlex

We measured tracheal flow from tracheal sounds to estimate tidal volume, minute ventilation (VI), respiratory frequency, mean inspiratory flow (VT/TI), and duty cycle (TI/Ttot). In 11 normal subjects, 3 patients with unstable airway obstruction, and 3 stable asthmatic patients, we measured tracheal sounds and flow twice: first to derive flow-sound relationships and second to obtain flow-volume relationships from the sound signal. The flow-volume relationship was compared with pneumotach-derived volume. When subjects were seated, facing forward and with neck rotation, flexion, and standing, flow-volume relationship was within 15% of pneumotach-derived volume. Error increased with neck extension and while supine. We then measured ventilation without mouthpiece or nose clip from tracheal sounds during quiet breathing for up to 30 min. Normal results +/- SD revealed tidal volume = 0.37 +/- 0.065 liter, respiratory frequency = 19.3 +/- 3.5 breaths/min, VI = 6.9 +/- 1.2 l/min, VT/TI = 0.31 +/- 0.06 l/s, and TI/Ttot = 0.37 +/- 0.04. Unstable airway obstruction had large VI due to increased VT/TI. With the exception of TI/Ttot, variations in ventilatory parameters were closer to log normal than normal distributions and tended to be greater in patients. We conclude that phonospirometry measures ventilation reasonably accurately without mouthpiece, nose clip, or rigid postural constraints.

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)0.000
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 score0.067
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.092
GPT teacher head0.307
Teacher spread0.215 · 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

Citations86
Published2002
Admission routes1
Has abstractyes

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