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Record W1801882481

Blood shift during cough in healthy subjects

2011· article· en· W1801882481 on OpenAlexaff
Antonella LoMauro, A. Pedotti, Peter T. Macklem, Andréa Aliverti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsRoyal Victoria HospitalMcGill University Health CentreRoyal Victoria Regional Health Centre
Fundersnot available
KeywordsMedicineFunctional residual capacityPlethysmographThorax (insect anatomy)Lung volumesAnesthesiaBlood volumeDiaphragmatic breathingBlood pressureCardiologyLungBreathingRespiratory systemInternal medicineAnatomyPathology
DOInot available

Abstract

fetched live from OpenAlex

Double Body Plethysmography (DBP), which combines total body plethysmography and opto-electronic Plethysmography, has been recently developed to measure the amount of blood displaced from the thorax to the extremities (Aliverti et al, PLoS One. 2009). By using DBP, we have recently shown that significant blood shifts (BS) occur during expulsive maneuvers and that abdominal pressure controls the outflow of blood from the splanchnic vasculature (Aliverti et al, J Appl Physiol, 2010). We hypothesized that also during cough a significant amount of blood can be displaced from the trunk to the extremities. We studied 7 healthy subjects (age: 28.6±2.5 yrs) during series of voluntary coughs at four different operating volumes: functional residual capacity (FRC), total lung capacity (TLC) and two intermediate volumes between FRC and TLC (namely, FRC+ and FRC++). BS from the thorax to the extremities were measured by DBP during quiet breathing and during cough at each operating lung volume. The results are shown in figure. BS during cough resulted significantly higher than during QB (p These findings might help to better understand the cardiopulmonary interactions during cough and the mechanism by which coughing during asystolic cardiac arrest can maintain consciousness in human subjects.

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 score0.491
Threshold uncertainty score0.576

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 head0.019
GPT teacher head0.207
Teacher spread0.189 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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