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Record W2588834921 · doi:10.1097/mcc.0000000000000387

High-frequency oscillatory ventilation: still a role?

2017· review· en· W2588834921 on OpenAlexaff
Jensen Ng, Niall D. Ferguson

Bibliographic record

VenueCurrent Opinion in Critical Care · 2017
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSinai Health SystemToronto Public Health
Fundersnot available
KeywordsMedicineIntensivistMean airway pressureARDSHigh-frequency ventilationIntensive care medicineHypoxemiaMechanical ventilationIntensive care unitAnesthesiaLungInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: In light of emerging data from clinical trials, the place of high-frequency oscillatory ventilation (HFOV) in the management of acute respiratory distress syndrome (ARDS) is uncertain. This review provides an overview of these new clinical data and also explores new areas of investigation for HFOV in adults. RECENT FINDINGS: While prior meta-analyses suggested benefit for HFOV, updated systematic reviews published this year, which include two large recent clinical trials, now show no statistically significant impact of HFOV on mortality in adults with ARDS. It is possible that HFOV would be safer and more effective with a more individualized approach to setting mean airway pressure (mPaw). Possible techniques to achieve this include titrating mPaw in response to oxygenation or hemodynamic changes after HFOV initiation, by measuring respiratory system impedance, or by following echocardiographic changes. SUMMARY: Although not first-line, HFOV remains a tool in the armamentarium of the intensivist managing the patient with severe ARDS and refractory hypoxemia. A refinement in the approach to delivering HFOV is warranted, with more attention paid to its adverse hemodynamic consequences.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.362
GPT teacher head0.518
Teacher spread0.156 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations28
Published2017
Admission routes1
Has abstractyes

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