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Record W3094159433 · doi:10.1080/21681163.2020.1835542

Improving central line needle insertions using <i>in-situ</i> vascular reconstructions

2020· article· en· W3094159433 on OpenAlexafffund
Leah Groves, Natalie Li, Blake VanBerlo, Natan Veinberg, Terry M. Peters, Elvis C. S. Chen

Bibliographic record

VenueComputer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · 2020
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchOntario Research Foundation
Keywords3D ultrasoundImaging phantomMedicineUltrasoundInternal jugular veinBiomedical engineeringRadiology

Abstract

fetched live from OpenAlex

We developed a neck central line insertion guidance system that renders 3D ultrasound (US) surface reconstructions of the carotid artery (CA) and internal jugular vein (IJV), a tracked model of the needle, and needle trajectory on a 2D monitor. Twenty clinicians evaluated this system compared to US-only guidance on a phantom using time and insertion accuracy metrics. The 3D system had a 100% success rate compared to 70% for the US-only system. The average distance from the centre line of the US reconstructed IJV was 1.8±0.9 mm under 3D guidance compared to 4.2±2.9 mm for US-only. Our system significantly improved needle insertion success rates and targeting accuracy compared to the US-only approach through a radiation-free surface reconstruction of the neck vascular structures. This work has the potential to provide a mobile 3D imaging and visualisation system for needle-based vascular interventions.

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)0.005
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.044
GPT teacher head0.379
Teacher spread0.335 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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