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Record W2969971968 · doi:10.1164/rccm.201903-0596so

Respiratory Drive in Critically Ill Patients. Pathophysiology and Clinical Implications

2019· article· en· W2969971968 on OpenAlexaff
Katerina Vaporidi, Evangelia Akoumianaki, Irene Telías, Ewan C. Goligher, Laurent Brochard, Dimitris Georgopoulos

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity Health NetworkUniversity of TorontoToronto General HospitalSt. Michael's Hospital
Fundersnot available
KeywordsMedicineCritically illIntensive care medicinePathophysiologyCritical illnessRespiratory systemIntensive careInternal medicine

Abstract

fetched live from OpenAlex

Respiratory drive, the intensity of the respiratory center's output, determines the effort exerted in each breath. The increasing awareness of the adverse effects of both strong and weak respiratory efforts during mechanical ventilation on patient outcome brings attention to the respiratory drive of the critically ill patient. Critical illness can affect patients' respiratory drive through multiple pathways, mainly operating through three feedback systems: cortical, metabolic, and chemical. The chemical feedback system, defined as the response of the respiratory center's output to changes in arterial blood gases and pH, is one of the most important determinants of respiratory drive. The purpose of this state-of-the-art review is to describe the determinants of respiratory drive in critically ill patients, review the tools available to assess respiratory drive at the bedside, and discuss the implications of altered respiratory drive during mechanical ventilation. An analysis that relates arterial carbon dioxide levels with brain's response to this stimulus will be presented, contrasting the brain's responses to the patient's ability to generate effective alveolar ventilation, both during unassisted breathing and with different modes of ventilatory assist. This analysis may facilitate comprehension of the pathophysiology of respiratory drive in critically ill patients. As we aim to avoid both over- and under-assistance with mechanical ventilation, considering the patients' respiratory drive at the bedside may improve clinical assessment and management of the patient and the ventilator.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.022
GPT teacher head0.353
Teacher spread0.331 · 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

Citations270
Published2019
Admission routes1
Has abstractyes

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