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Record W3102749014 · doi:10.1115/1.4049088

Design Synthesis and Preliminary Evaluation of a Novel Tool to Noninvasively Characterize Pressurized, Physiological Vessels

2020· article· en· W3102749014 on OpenAlexafffund
Natasha Jacobson, Mark Driscoll

Bibliographic record

VenueJournal of Medical Devices · 2020
Typearticle
Languageen
FieldMedicine
TopicAbdominal Surgery and Complications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupine positionBiomedical engineeringMedicineAbdominal wallElasticity (physics)Intensive careSurgeryMaterials scienceIntensive care medicineComposite material

Abstract

fetched live from OpenAlex

Abstract A prolonged increase in intra-abdominal pressure (IAP) is life-threatening, yet commonly seen in intensive care units (ICUs). Despite this, existing clinically accepted IAP measurement techniques are invasive and not inter-rater reliable. As such, it is the effort of this research to develop a direct, noninvasive, handheld tool to measure internal pressures in pressurized, physiological vessels. The novel device uses a localized known pressure (namely, aspiration) to measure resulting tissue deformation, from which internal pressures can be divulged considering the extended Hencky solution. Two male participants were tested with the device to confirm feasibility of the theoretical device function for IAP measurement. Participants' Young's moduli of the abdominal wall were calculated with measured IAP values. Results were consistent with participant body mass indices and overall health. Average measured IAP was 0.42 kPa and 0.46 kPa at supine and inclined positions, respectively. Average measured abdominal wall elasticity was 14.91 kPa and 23.09 kPa at supine and inclined positions, respectively. These preliminary findings suggest the potential use of the device described herein as a measurement system for pressurized vessels, whereas the system will be tested on a larger sample size before recommending clinical use.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
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 head0.168
GPT teacher head0.346
Teacher spread0.178 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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