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Record W1997651109 · doi:10.1117/12.712701

A hardware and software protocol for the evaluation of electromagnetic tracker accuracy in the clinical environment: a multi-center study

2007· article· en· W1997651109 on OpenAlexafffund
Emmanuel Wilson, Ziv Yaniv, Hui Zhang, Christopher Nafis, Eric Shen, Guy Shechter, Andrew D. Wiles, Terry M. Peters, David Lindisch, Kevin Cleary

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsRobarts Clinical TrialsWestern University
FundersCanadian Institutes of Health ResearchGeorgetown University
KeywordsImaging phantomComputer scienceProtocol (science)SoftwareElectromagnetic interferenceElectromagnetic environmentAccuracy and precisionComputer hardwarePhysicsOpticsMedicine

Abstract

fetched live from OpenAlex

This paper proposes an assessment protocol that incorporates both hardware and analysis methods for evaluation of electromagnetic tracker accuracy in different clinical environments. The susceptibility of electromagnetic tracker measurement accuracy is both highly dependent on nearby ferromagnetic interference sources and non-isotropic. These inherent limitations combined with the various hardware components and assessment techniques used within different studies makes the direct comparison of measurement accuracy between studies difficult. This paper presents a multicenter study to evaluate electromagnetic devices in different clinical environments using a common hardware phantom and assessment techniques so that results are directly comparable. Measurement accuracy has been shown to be in the range of 0.79-6.67mm within a 180mm<sup>3</sup> sub-volume of the Aurora measurement space in five different clinical environments.

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)0.038
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.038
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.048
GPT teacher head0.351
Teacher spread0.303 · 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 designObservational
Domainnot available
GenreMethods

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

Citations49
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicRadiation Dose and ImagingFrench-language works237,207