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Record W2884652417 · doi:10.1115/dmd2018-6838

A Device for Improving Oxygenation in Patients With Acute Respiratory Distress Syndrome

2018· article· en· W2884652417 on OpenAlexaff
Alex J. Gordon, Kevin Luo, Rami Saab, Doreen Engelberts, Brian P. Kavanagh, Takeshi Yoshida, Thomas Looi

Bibliographic record

Venue2018 Design of Medical Devices Conference · 2018
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsARDSMedicineFraction of inspired oxygenOxygenationAnesthesiaAcute respiratory distressDiaphragm (acoustics)LungBiomedical engineeringIntensive care medicineInternal medicineMechanical ventilation

Abstract

fetched live from OpenAlex

In this paper we present a device for improving blood oxygenation in patients with Acute Respiratory Distress Syndrome (ARDS). ARDS is caused by lung-related illness or injury, and can occur in mechanically ventilated ICU patients due to volutrauma or barotrauma. In ARDS, the lower lung is closed resulting in impaired gas exchange, and the upper lung is easily overstretched resulting in injury. The application of continuous negative abdominal pressure (CNAP) assists in opening the lower lung by pulling the diaphragm towards the abdomen. The device, consisting of a rigid arch, a compliant patient interface, and a pressure sensor module, allows for the application of CNAP to a patient suffering from ARDS. An initial pig trial using the prototype device showed significant improvement in the ratio of oxygen in the blood to the fraction of inspired oxygen, PaO2/FiO2, after five minutes of −5 cmH2O pressure application. Furthermore, preliminary testing on healthy humans indicated the device was comfortable, easy to apply, and formed a consistent airtight seal. Future prototypes will focus on ease of application, rigidity, and adjustability.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.041
GPT teacher head0.296
Teacher spread0.255 · 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 designObservational
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

Citations1
Published2018
Admission routes1
Has abstractyes

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