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Record W2947518705 · doi:10.4187/respcare.06998

Ventilation During Cardiopulmonary Resuscitation: What Have We Learned From Models?

2019· review· en· W2947518705 on OpenAlexaff
Emmanuel Charbonney, Domenico Luca Grieco, Ricardo Luiz Cordioli, Bilal Badat, Dominique Savary, Jean-Christophe M. Richard

Bibliographic record

VenueRespiratory Care · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversité de MontréalUniversité du Québec à Trois-RivièresUniversité du Québec à Montréal
Fundersnot available
KeywordsMedicineCardiopulmonary resuscitationVentilation (architecture)AirwayResuscitationIntensive care medicineRespiratory physiologyAnesthesiaLungInternal medicine

Abstract

fetched live from OpenAlex

The optimization of ventilation during cardiopulmonary resuscitation (CPR) is a broad field of research. Recent physiological observations in this field challenge the current understanding of respiratory and circulatory interactions. Thanks to different models available (bench, animal, human), the understanding of physiological phenomena occurring during CPR has progressed. In this review, we describe the clinical observations that have led to the emerging concept of lung volume reduction and associated thoracic airway closure. We summarize the clinical and animal observations supporting these concepts. We then discuss the different contributions of bench, animal, and human models to the understanding of airway closure and their impact on intrathoracic pressure, airway closure, and hemodynamics generated by chest compression. The limitation of airway pressure and ventilation, resulting from airway closure reproducible in models, may play a major role in ventilation and gas exchange impairment observed during prolonged resuscitation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.109
GPT teacher head0.348
Teacher spread0.239 · 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

Citations31
Published2019
Admission routes1
Has abstractyes

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