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Record W4311279532 · doi:10.1038/s41551-022-00971-6

An implantable soft robotic ventilator augments inspiration in a pig model of respiratory insufficiency

2022· article· en· W4311279532 on OpenAlexfundno aff
Lucy Hu, Jean Bonnemain, Mossab Y. Saeed, Manisha Singh, Diego Quevedo Moreno, Nikolay V. Vasilyev, Ellen T. Roche

Bibliographic record

VenueNature Biomedical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsnot available
FundersCIHR Skin Research Training CentreNational Science FoundationCanadian Institutes of Health ResearchDivision of Chemical, Bioengineering, Environmental, and Transport SystemsNational Institute of Biomedical Imaging and BioengineeringDirectorate for EngineeringMuscular Dystrophy AssociationCentre Hospitalier Universitaire VaudoisNational Institutes of HealthU.S. Department of Health and Human ServicesGovernment of CanadaMassachusetts Institute of Technology
KeywordsRespiratory systemMedicineMechanical ventilatorIntensive care medicineCardiologyMechanical ventilationInternal medicine

Abstract

fetched live from OpenAlex

Severe diaphragm dysfunction can lead to respiratory failure and to the need for permanent mechanical ventilation. Yet permanent tethering to a mechanical ventilator through the mouth or via tracheostomy can hinder a patient's speech, swallowing ability and mobility. Here we show, in a porcine model of varied respiratory insufficiency, that a contractile soft robotic actuator implanted above the diaphragm augments its motion during inspiration. Synchronized actuation of the diaphragm-assist implant with the native respiratory effort increased tidal volumes and maintained ventilation flow rates within the normal range. Robotic implants that intervene at the diaphragm rather than at the upper airway and that augment physiological metrics of ventilation may restore respiratory performance without sacrificing quality of life.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations48
Published2022
Admission routes1
Has abstractyes

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