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Record W4390508062 · doi:10.1186/s13054-023-04779-x

Analysis and applications of respiratory surface EMG: report of a round table meeting

2024· article· en· W4390508062 on OpenAlexaff
Annemijn H. Jonkman, R. S. P. Warnaar, Walter Baccinelli, Niklas M. Carbon, Rebecca D’Cruz, Jonne Doorduin, Jeroen L. M. van Doorn, Judith Elshof, Jan Graßhoff, Leo Heunks, Alette A. Koopman, Daniël Langer, Candace Moore, Juan Martín Núñez Silveira, Eike Petersen, Diego Poddighe, Michelle Ramsay, Antenor Rodrigues, Lisanne H. Roesthuis, Anne Rossel, Abel Torres, Marieke L. Duiverman, Eline Oppersma

Bibliographic record

VenueCritical Care · 2024
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsnot available
FundersNational Institute for Health and Care ResearchLorentz CenterNetherlands eScience Center
KeywordsMedicineStandardizationPhysical medicine and rehabilitationClinical PracticeRespiratory failureElectromyographyMechanical ventilationRespiratory systemAcute respiratory failureIntensive care medicinePhysical therapyInternal medicineComputer science

Abstract

fetched live from OpenAlex

Surface electromyography (sEMG) can be used to measure the electrical activity of the respiratory muscles. The possible applications of sEMG span from patients suffering from acute respiratory failure to patients receiving chronic home mechanical ventilation, to evaluate muscle function, titrate ventilatory support and guide treatment. However, sEMG is mainly used as a monitoring tool for research and its use in clinical practice is still limited-in part due to a lack of standardization and transparent reporting. During this round table meeting, recommendations on data acquisition, processing, interpretation, and potential clinical applications of respiratory sEMG were discussed. This paper informs the clinical researcher interested in respiratory muscle monitoring about the current state of the art on sEMG, knowledge gaps and potential future applications for patients with respiratory failure.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.017

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 head0.017
GPT teacher head0.291
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations67
Published2024
Admission routes1
Has abstractyes

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