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Record W2596807004 · doi:10.1164/rccm.201612-2495ci

F <scp>ifty</scp> Y <scp>ears of</scp> R <scp>esearch in</scp> ARDS. Respiratory Mechanics in Acute Respiratory Distress Syndrome

2017· review· en· W2596807004 on OpenAlexaff
William R. Henderson, Lü Chen, Marcelo B. P. Amato, Laurent Brochard

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of TorontoSt. Michael's HospitalUniversity of British Columbia
Fundersnot available
KeywordsARDSMedicineRespiratory systemRespiratory distressRespiratory physiologyAcute respiratory distressLungAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Acute respiratory distress syndrome is a multifactorial lung injury that continues to be associated with high levels of morbidity and mortality. Mechanical ventilation, although lifesaving, is associated with new iatrogenic injury. Current best practice involves the use of small Vt, low plateau and driving pressures, and high levels of positive end-expiratory pressure. Collectively, these interventions are termed "lung-protective ventilation." Recent investigations suggest that individualized measurements of pulmonary mechanical variables rather than population-based ventilation prescriptions may be used to set the ventilator with the potential to improve outcomes beyond those achieved with standard lung protective ventilation. This review outlines the measurement and application of clinically applicable pulmonary mechanical concepts, such as plateau pressures, driving pressure, transpulmonary pressures, stress index, and measurement of strain. In addition, the concept of the "baby lung" and the utility of dynamic in addition to static measures of pulmonary mechanical variables are discussed.

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)0.007
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.028
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0110.002
Bibliometrics0.0040.002
Science and technology studies0.0000.004
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.006
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 head0.070
GPT teacher head0.392
Teacher spread0.322 · 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; both teacher heads agree on what is shown here.

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

Citations195
Published2017
Admission routes1
Has abstractyes

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