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Record W3028717465 · doi:10.4187/respcare.07410

Assessing Diaphragmatic Function

2020· review· en· W3028717465 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRespiratory Care · 2020
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchEuropean Respiratory Society
KeywordsMedicineDiaphragm (acoustics)Mechanical ventilationDiaphragmatic breathingWeaknessIntensive care medicineMuscle atrophyPhysical medicine and rehabilitationAtrophyAnesthesiaSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

The diaphragm is vulnerable to injury during mechanical ventilation, and diaphragm dysfunction is both a marker of severity of illness and a predictor of poor patient outcome in the ICU. A combination of factors can result in diaphragm weakness. Both insufficient and excessive diaphragmatic contractile effort can cause atrophy or injury, and recent evidence suggests that targeting an appropriate amount of diaphragm activity during mechanical ventilation has the potential to mitigate diaphragm dysfunction. Several monitoring tools can be used to assess diaphragm activity and function during mechanical ventilation, including pressure-derived parameters, electromyography, and ultrasound. This review details these techniques and presents the rationale for a diaphragm-protective ventilation strategy.

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.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.114
GPT teacher head0.398
Teacher spread0.285 · 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