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Record W2953238081 · doi:10.29390/cjrt-2019-004

High-frequency oscillatory ventilation: A narrative review

2019· review· en· W2953238081 on OpenAlexvenueno aff
Morgan Myers, Nathan Rodrigues, Arzu Ari

Bibliographic record

VenueCanadian Journal of Respiratory Therapy · 2019
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsARDSMedicineVentilation (architecture)Mechanical ventilationHigh-frequency ventilationMean airway pressureOxygenationAnesthesiaIntensive care medicineLungInternal medicine

Abstract

fetched live from OpenAlex

High-frequency oscillatory ventilation (HFOV) is a lung-protective strategy that can be utilized in the full spectrum of patient populations ranging from neonatal to adults with acute lung injury. HFOV is often utilized as a rescue strategy when conventional mechanical ventilation (CV) has failed. HFOV uses low tidal volumes and constant mean airway pressures in conjunction with high respiratory rates to provide beneficial effects on oxygenation and ventilation, while eliminating the traumatic "inflate-deflate" cycle imposed by CV. Although statistical evidence supporting HFOV is particularly low, potential benefits for its application in many clinical manifestations still remain. High-frequency oscillation is a safe and effective rescue mode of ventilation for the treatment of acute respiratory distress syndrome (ARDS). All patients who have ventilator-induced lung injury (VILI) or are at risk of developing VILI or ARDS would be suitable candidates for HFOV, especially those who have failed conventional mechanical ventilation. This narrative aims to provide a review of HFOV vis-à-vis its indications, contraindications, hazards, parameters to monitoring, patient selection, clinical goals, mechanisms of action, controls for optimizing ventilation and oxygenation, clinical application in ARDS, and a comparison with other modes of mechanical ventilation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.105
GPT teacher head0.361
Teacher spread0.256 · 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

Citations71
Published2019
Admission routes1
Has abstractyes

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