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Record W2541706955 · doi:10.1164/rccm.201604-0748cp

F <scp>ifty</scp> Y <scp>ears of</scp> R <scp>esearch in</scp> ARDS. Spontaneous Breathing during Mechanical Ventilation. Risks, Mechanisms, and Management

2016· review· en· W2541706955 on OpenAlexaff
Takeshi Yoshida, Yuji Fujino, Marcelo B. P. Amato, Brian P. Kavanagh

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2016
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsARDSMedicineMechanical ventilationVentilation (architecture)Diaphragm (acoustics)AnesthesiaIntensive care medicineLungInternal medicine

Abstract

fetched live from OpenAlex

Spontaneous respiratory effort during mechanical ventilation has long been recognized to improve oxygenation, and because oxygenation is a key management target, such effort may seem beneficial. Also, disuse and loss of peripheral muscle and diaphragm function is increasingly recognized, and thus spontaneous breathing may confer additional advantage. Reflecting this, epidemiologic data suggest that the use of partial (vs. full) support modes of ventilation is increasing. Notwithstanding the central place of spontaneous breathing in mechanical ventilation, accumulating evidence indicates that it may cause-or worsen-acute lung injury, especially if acute respiratory distress syndrome is severe and spontaneous effort is vigorous. This Perspective reviews the evidence for this phenomenon, explores mechanisms of injury, and provides suggestions for clinical management and future research.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.004

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 head0.040
GPT teacher head0.355
Teacher spread0.315 · 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 designSystematic review
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

Citations342
Published2016
Admission routes1
Has abstractyes

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