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Record W3076965332 · doi:10.4187/respcare.08296

A Gas-Powered, Patient-Responsive Automatic Resuscitator for Use in Acute Respiratory Failure: A Bench and Experimental Study

2020· article· en· W3076965332 on OpenAlexafffundabout
Annemijn H. Jonkman, Bhushan H. Katira, Annia Schreiber, Cong Lü, Doreen Engelberts, Fernando Nataniel Vieira, Alexandra Marquez, Arthur S. Slutsky, Paul Dorian, Laurent Brochard

Bibliographic record

VenueRespiratory Care · 2020
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSickKids FoundationHospital for Sick ChildrenChildren's Hospital of Eastern OntarioUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsMedicineVentilation (architecture)Respiratory physiologyAnesthesiaMechanical ventilationOxygenationPulmonary complianceLungRespiratory systemPeak inspiratory pressureTidal volumeInternal medicineMechanical engineering

Abstract

fetched live from OpenAlex

BACKGROUND: During the COVID-19 pandemic, a need for innovative, inexpensive, and simple ventilator devices for mass use has emerged. The Oxylator (CPR Medical Devices, Markham, Ontario, Canada) is an FDA-approved, fist-size, portable ventilation device developed for out-of-hospital emergency ventilation. It has not been tested in conditions of severe lung injury or with added PEEP. We aimed to assess the performance and reliability of the device in simulated and experimental conditions of severe lung injury, and to derive monitoring methods to allow the delivery of safe, individualized ventilation during situations of surge. METHODS: We bench-tested the functioning of the device with an added PEEP valve extensively, mimicking adult patients with various respiratory mechanics during controlled ventilation, spontaneous breathing, and prolonged unstable conditions where mechanics or breathing effort was changed at every breath. The device was further tested on a porcine model (4 animals) after inducing lung injury, and these results were compared with conventional ventilation modes. RESULTS: The device was stable and predictable, delivering a constant flow (30 L/min) and cycling automatically at the inspiratory pressure set (minimum of 20 cm H 2 O) above auto-PEEP. Changes in respiratory mechanics manifested as changes in respiratory timing, allowing prediction of tidal volumes from breathing frequency. Simulating lung injury resulted in relatively low tidal volumes (330 mL with compliance of 20 mL/cm H 2 O). In the porcine model, arterial oxygenation, CO 2 , and pH were comparable to conventional modes of ventilation. CONCLUSIONS: The Oxylator is a simple device that delivered stable ventilation with tidal volumes within a clinically acceptable range in bench and porcine lung models with low compliance. External monitoring of respiratory timing is advisable, allowing tidal volume estimation and recognition of changes in respiratory mechanics. The device can be an efficient, low-cost, and practical rescue solution for providing short-term ventilatory support as a temporary bridge, but it requires a caregiver at the bedside.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.304
Teacher spread0.268 · 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

Citations7
Published2020
Admission routes3
Has abstractyes

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