MétaCan
Menu
Back to cohort
Record W4410361987 · doi:10.1080/17434440.2025.2504454

Diaphragm neurostimulation in mechanical ventilation: current status and future prospects

2025· review· en· W4410361987 on OpenAlexaff
Elizabeth Rohrs, Steven Reynolds, Martin Dres

Bibliographic record

VenueExpert Review of Medical Devices · 2025
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSimon Fraser UniversityFraser Health
Fundersnot available
KeywordsDiaphragm (acoustics)NeurostimulationMechanical ventilationMedicineCurrent (fluid)Biomedical engineeringAnesthesiaInternal medicineAcousticsStimulationEngineeringPhysics

Abstract

fetched live from OpenAlex

INTRODUCTION: Diaphragm neurostimulation is a muscle stimulation technique that, through electrodes placed directly on or at the vicinity of the phrenic nerves, induces diaphragm contractions independently of the patient's cooperation. Recently, the technical development of temporary diaphragm neurostimulation devices has paved the way for a new era in the management of critically ill patients. AREAS COVERED: In this review, we describe the latest technical developments in diaphragm neurostimulation and its physiological effects. We searched MEDLINE of experimental and clinical studies in English language published from database inception until 31 October 2024. We also discuss the advances in terms of patients centered outcomes and the key areas for improvement. Lastly, we introduce possible future directions and the novel improvements in patient care. EXPERT OPINION: The research on diaphragm neurostimulation promise as an emerging intervention which addresses common complications associated with mechanical ventilation. Large-scale clinical trials are necessary to validate diaphragm neurostimulation efficacy and safety in humans, establish treatment protocols, and determine cost-effectiveness, all of which are essential for diaphragm neurostimulation to be widely accepted in clinical practice.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.720
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.418
Teacher spread0.385 · 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 designOther design
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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueExpert Review of Medical DevicesSame topicRespiratory Support and MechanismsFrench-language works237,207