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Record W4415804431 · doi:10.1080/25310429.2025.2572252

Diaphragm ultrasound for muscle strength assessment: A systematic literature review

2025· article· en· W4415804431 on OpenAlexaboutno aff
João Leote, Margarida Monteiro, Cláudia Rocha, Carolina F. Rodrigues, Marco Pereira, Maria da Luz Antunes, Hermínia Brites Dias

Bibliographic record

VenuePulmonology · 2025
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsSystematic reviewDiaphragm (acoustics)Diaphragmatic breathingUltrasoundMuscle strengthDiaphragm muscle

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess if diaphragmatic ultrasound (DU) reflects diaphragmatic muscle strenght when compared to respiratory tests and neurophysiological studies. METHODS: A systematic literature review was conducted on adults undergoing DU, compared to any respiratory or neurophysiological technique. The search strategy was applied in PubMed, Scopus, and Web of Science, and the analysis was conducted using the PRISMA methodology. Three eligibility assessment stages were performed: title, abstract, and full-text reading. The risk of bias was evaluated using the RoB 2.0, ROBINS-I, and Newcastle-Ottawa Scale tools. RESULTS: Out of 155 identified articles, 25 were selected for full-text review (14 non-randomised studies, 8 case-control studies, and 3 randomised studies). The overall risk of bias was moderate, with the main biases related to population selection and intervention assessment.Twenty-three articles used maximal inspiratory pressure measurement as a comparator which showed a weak-to-moderate correlation, significant in 10 studies, with diaphragmatic excursion. Three studies reported a weak association between diaphragmatic thickening and sniff pressure.Five articles reported a concordant correlation between diaphragmatic thickening and compound muscle action potential amplitude, significant only in one study. CONCLUSION: The variability of results obtained across different pathologies does not support the use of DU alone to predict diaphragmatic muscle strength.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.529
Threshold uncertainty score0.378

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 integrity0.0000.000
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 head0.018
GPT teacher head0.374
Teacher spread0.356 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

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