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Record W2791376757 · doi:10.1155/2018/6431749

Accuracy and Precision of a Veterinary Neuronavigation System for Radiation Oncology Positioning

2018· article· en· W2791376757 on OpenAlexaff
Isabelle F. Vanhaezebrouck, Elizabeth A. Ballegeer, Stephen Frey, Rob Sieffert

Bibliographic record

VenueJournal of Veterinary Medicine · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsRogue Research (Canada)
FundersCollege of Engineering, Michigan State UniversityMichigan State University
KeywordsRadiosurgeryRadiation oncologyNeuronavigationMedical physicsPositioning systemMedicineRadiologyRadiation therapyNuclear medicinePhysicsMagnetic resonance imagingAcoustics

Abstract

fetched live from OpenAlex

Conformal radiation treatment plans such as IMRT and other radiosurgery techniques require very precise patient positioning, typically within a millimeter of error for best results. CT cone beam, real-time navigation, and infrared position sensors are potential options for success but rarely present in veterinary radiation centers. A neuronavigation system (Brainsight Vet, Rogue Research) was tested 22 times on a skull for positioning accuracy and precision analysis. The first 6 manipulations allowed the authors to become familiar with the system but were still included in the analyses. Overall, the targeting mean error in 3D was 1.437 mm with SD 1.242 mm. This system could be used for positioning for radiation therapy or radiosurgery.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.035
GPT teacher head0.393
Teacher spread0.358 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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