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Record W2988605660 · doi:10.1097/mcc.0000000000000683

Diaphragm protection: what should we target?

2019· review· en· W2988605660 on OpenAlexaff
Tom Schepens, José Dianti

Bibliographic record

VenueCurrent Opinion in Critical Care · 2019
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDiaphragm (acoustics)Mechanical ventilationMedicineWeaknessVentilation (architecture)Respiratory systemIntensive care medicineAnesthesiaSurgeryInternal medicineEngineering

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Diaphragm weakness can impact survival and increases comorbidities in ventilated patients. Mechanical ventilation is linked to diaphragm dysfunction through several mechanisms of injury, referred to as myotrauma. By monitoring diaphragm activity and titrating ventilator settings, the critical care clinician can have a direct impact on diaphragm injury. RECENT FINDINGS: Both the absence of diaphragm activity and excessive inspiratory effort can result in diaphragm muscle weakness, and recent evidence demonstrates that a moderate level of diaphragm activity during mechanical ventilation improves ICU outcome. This supports the hypothesis that by avoiding ventilator overassistance and underassistance, the clinician can implement a diaphragm-protective ventilation strategy. Furthermore, eccentric diaphragm contractions and end-expiratory shortening could impact diaphragm strength as well. This review describes these potential targets for diaphragm protective ventilation. SUMMARY: A ventilator strategy that results in appropriate levels of diaphragm activity has the potential to be diaphragm-protective and improve clinical outcome. Monitoring respiratory effort during mechanical ventilation is becoming increasingly important.

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 score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.515
GPT teacher head0.525
Teacher spread0.010 · 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

Citations29
Published2019
Admission routes1
Has abstractyes

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