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Chest wall kinematic determinants of diaphragm length by optoelectronic plethysmography and ultrasonography

2003· article· en· W2154211376 on OpenAlexaff
Andréa Aliverti, G. Ghidoli, Raffaele L. Dellacá, A. Pedotti, Peter T. Macklem

Bibliographic record

VenueJournal of Applied Physiology · 2003
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsRib cageDiaphragm (acoustics)AnatomyPlethysmographAbdomenThorax (insect anatomy)Transverse planeMedicineBreathingDiaphragmatic breathingNuclear medicineBiomedical engineeringCardiologyPhysicsAcousticsPathology

Abstract

fetched live from OpenAlex

To estimate diaphragm fiber length from thoracoabdominal configuration, we measured axial motion of the right-sided area of apposition by ultrasonography and volumes displaced by chest wall compartments [pulmonary, abdominal rib cage, and abdomen (Vab)] by optoelectronic plethysmography in four normal men during quiet breathing and incremental exercise without and with expiratory flow limitation. Points at the cephalic area of apposition border were digitized from echo images and mapped into three-dimensional space, and the axial distance from the xyphoidal transverse plane (D(ap)) was measured simultaneously with the volumes. Linear regression analysis between changes (Delta) in D(ap) and the measured volume changes under all conditions showed that 1) DeltaD(ap) was linearly related more to DeltaVab than to changes in pulmonary and abdominal rib cage volumes; and 2) this was highly repeatable between measures. Multiple stepwise regression analysis showed that DeltaVab accounted for 89-96% of the variability of DeltaD(ap), whereas the rib cage compartments added <1%. We conclude that, under conditions of quiet breathing and exercise, with and without expiratory flow limitation, instantaneous DeltaD(ap) can be estimated from DeltaVab.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.262
Teacher spread0.254 · 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

Citations68
Published2003
Admission routes1
Has abstractyes

Explore more

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