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Record W4405114429 · doi:10.1097/mcc.0000000000001237

Proportional modes to hasten weaning

2024· review· en· W4405114429 on OpenAlexaff
Karen J. Bosma

Bibliographic record

VenueCurrent Opinion in Critical Care · 2024
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineMechanical ventilationWeaningVentilation (architecture)Intensive care unitRandomized controlled trialIntensive care medicineEmergency medicineIntensive careMeta-analysisPressure support ventilationAnesthesiaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The purpose of this review is to examine the current state of the evidence, including several recent systematic reviews and meta-analyses, to determine if proportional modes of ventilation have the potential to hasten weaning from mechanical ventilation for adult critically ill patients, compared to pressure support ventilation (PSV), the current standard of care during the recovery and weaning phases of mechanical ventilation. RECENT FINDINGS: Proportional assist ventilation (PAV) and neurally adjusted ventilatory assist (NAVA) are two commercially available proportional modes that have been studied in randomized controlled trials (RCTs). Although several feasibility studies were not powered to detect differences in clinical outcomes, emerging evidence suggests that both PAV and NAVA may reduce duration of mechanical ventilation, intensive care unit (ICU) length of stay, and hospital mortality compared to PSV, as shown in some small, primarily single-centre studies. Recent meta-analyses suggest that PAV shortens duration of mechanical ventilation and improves weaning success rate, and NAVA may reduce ICU and hospital mortality. SUMMARY: The current state of the evidence suggests that proportional modes may hasten weaning from mechanical ventilation, but larger, multicentre RCTS are needed to confirm these preliminary findings.

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 score0.911
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.412
GPT teacher head0.551
Teacher spread0.138 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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