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Record W3030745188 · doi:10.4187/respcare.07467

Lung Volume Measurement and Ventilation Distribution During Invasive Mechanical Ventilation

2020· review· en· W3030745188 on OpenAlexaff
Thomas Piraino

Bibliographic record

VenueRespiratory Care · 2020
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineVentilation (architecture)Lung volumesMechanical ventilationFunctional residual capacityLungPulmonary complianceIntensive care medicineElectrical impedance tomographyAnesthesiaRadiologyTomographyInternal medicineMechanical engineering

Abstract

fetched live from OpenAlex

Lung volume measurement performed during invasive mechanical ventilation can be used to determine functional residual capacity, changes in end-expiratory lung volume with the application of PEEP, and lung strain. However, many bedside measurements provide useful information without the use of specialized equipment. Ventilation distribution through the lung has traditionally been assessed with computed tomography, but more recently electrical impedance tomography has brought the ability to monitor this at the bedside, and without exposure to radiation. This review will describe techniques to measure lung volumes in the ICU and the relationship between lung strain, stress, and other measurements. This review will also discuss monitoring ventilation distribution at the bedside and the clinical assessment of regional compliance that this technology provides.

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 score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.058
GPT teacher head0.315
Teacher spread0.257 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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