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Record W2795039277 · doi:10.1097/mcc.0000000000000494

Esophageal pressure monitoring: why, when and how?

2018· review· en· W2795039277 on OpenAlexaff
Takeshi Yoshida, Laurent Brochard

Bibliographic record

VenueCurrent Opinion in Critical Care · 2018
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsTranspulmonary pressureMedicineMechanical ventilationAnesthesiaPlateau pressureLungCardiologyInternal medicineLung volumesARDS

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Esophageal manometry has shown its usefulness to estimate transpulmonary pressure, that is lung stress, and the intensity of spontaneous effort in patients with acute respiratory distress syndrome. However, clinical uptake of esophageal manometry in ICU is still low. Thus, the purpose of review is to describe technical tips to adequately measure esophageal pressure at the bedside, and then update the most important clinical applications of esophageal manometry in ICU. RECENT FINDINGS: Each esophageal balloon has its own nonstressed volume and it should be calibrated properly to measure pleural pressure accurately: transpulmonary pressure calculated on absolute esophageal pressure reflects values in the lung regions adjacent to the esophageal balloon (i.e. dependent to middle lung). Inspiratory transpulmonary pressure calculated from airway plateau pressure and the chest wall to respiratory system elastance ratio reasonably reflects lung stress in the nondependent 'baby' lung, at highest risk of hyperinflation. Also esophageal pressure can be used to detect and minimize patient self-inflicted lung injury. SUMMARY: Esophageal manometry is not a complicated technique. There is a large potential to improve clinical outcome in patients with acute respiratory distress syndrome, acting as an early detector of risk of lung injury from mechanical ventilation and vigorous spontaneous effort.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score1.000

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 head0.295
GPT teacher head0.488
Teacher spread0.193 · 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 designNot applicable
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

Citations50
Published2018
Admission routes1
Has abstractyes

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