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Respiratory muscle dynamics and control during exercise with externally imposed expiratory flow limitation

2002· article· en· W2090853303 on OpenAlexaff
Andréa Aliverti, Iacopo Iandelli, Roberto Duranti, S. J. Cala, Bengt Kayser, Susan J. Kelly, Gianni Misuri, A. Pedotti, G. Scano, Paweł Śliwiński, Sheng Yan, Peter T. Macklem

Bibliographic record

VenueJournal of Applied Physiology · 2002
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsHypercapniaDiaphragm (acoustics)Rib cageRespiratory systemMedicineCardiologyAnatomyInternal medicineControl of respirationLung volumesPlethysmographVentilation (architecture)RespirationAnesthesiaLungChemistryPhysics

Abstract

fetched live from OpenAlex

To determine how decreasing velocity of shortening (U) of expiratory muscles affects breathing during exercise, six normal men performed incremental exercise with externally imposed expiratory flow limitation (EFLe) at approximately 1 l/s. We measured volumes of chest wall, lung- and diaphragm-apposed rib cage (Vrc,p and Vrc,a, respectively), and abdomen (Vab) by optoelectronic plethysmography; esophageal, gastric, and transdiaphragmatic pressures (Pdi); and end-tidal CO2 concentration. From these, we calculated velocity of shortening and power (W) of diaphragm, rib cage, and abdominal muscles (di, rcm, ab, respectively). EFLe forced a decrease in Uab, which increased Pab and which lasted well into inspiration. This imposed a load, overcome by preinspiratory diaphragm contraction. Udi and inspiratory Urcm increased, reducing their ability to generate pressure. Pdi, Prcm, and Wab increased, indicating an increased central drive to all muscle groups secondary to hypercapnia, which developed in all subjects. These results suggest a vicious cycle in which EFLe decreases Uab, increasing Pab and exacerbating the hypercapnia, which increases central drive increasing Pab even more, leading to further CO2 retention, and so forth.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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 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

Citations90
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied PhysiologySame topicChronic Obstructive Pulmonary Disease (COPD) ResearchFrench-language works237,207