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Record W4409340826 · doi:10.1097/mcc.0000000000001276

Monitoring and preserving diaphragmatic function in mechanical ventilation

2025· review· en· W4409340826 on OpenAlexaff
Margot Combet, Briar Coman, Irene Telías

Bibliographic record

VenueCurrent Opinion in Critical Care · 2025
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineMechanical ventilationDiaphragm (acoustics)WeaknessPhrenic nerveIntensive care medicineDiaphragmatic breathingAdverse effectRehabilitationMuscle weaknessPhysical medicine and rehabilitationPhysical therapyRespiratory systemAnesthesiaSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review summarizes the evidence on clinical outcomes related to diaphragm dysfunction, providing an overview on available monitoring tools and strategies for its prevention and treatment. RECENT FINDINGS: Long-term adverse functional outcomes in intensive care survivors are well documented, especially in patients with prolonged mechanical ventilation. Because diaphragm weakness is highly prevalent and strongly associated with weaning failure, a link between diaphragm weakness and adverse functional outcomes is probable. Mechanisms of critical illness-associated diaphragm weakness are complex and include ventilator-related myotrauma through various pathways (i.e. over-assistance, under-assistance, eccentric, expiratory). Given this potential clinical impact, research on preventive and therapeutic strategies is growing, including the development of ventilation strategies aiming at protecting both the lung and the diaphragm. Phrenic nerve stimulation and specific rehabilitation strategies also appear promising. SUMMARY: Diaphragm dysfunction is associated with adverse clinical outcomes in ventilated patients; therefore, their inspiratory effort and function should be monitored. Whenever possible, and without compromising lung protection, moderate inspiratory effort should be targeted. Phrenic nerve stimulation and specific rehabilitation strategies are promising to prevent and treat diaphragm dysfunction, but further evidence is needed before widespread implementation.

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)0.001
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 score0.780
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.238
GPT teacher head0.498
Teacher spread0.259 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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