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Record W4381492515 · doi:10.1364/brain.2023.bth2b.6

Human Neuronavigation Using Coherent Anti-Sokes Raman Scattering Spectroscopy and Diffuse Reflectance Spectroscopy for Deep Brain Stimulation Surgery

2023· article· en· W4381492515 on OpenAlexaff
Mireille Quémener, Alexandre Bédard, Thomas Charland, Damon DePaoli, Valérie Dionne, Anthony Drouin, Séabstien Jerczynski, Shadi Masoumi, Elahe Parham, Anaïs Parrot, Valérie Pineau Noël, Antoine Rousseau, Jonathan Roussel, Léo Cantin, Martin Parent, Daniel Côté

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsHôpital de l'Enfant-JésusCentre hospitalier de l'Université LavalUniversité Laval
Fundersnot available
KeywordsDiffuse reflectance infrared fourier transformNeuronavigationRaman spectroscopyRaman scatteringWhite matterSpectroscopyDeep brain stimulationNuclear magnetic resonanceDiffuse reflectionOpticsMaterials scienceNuclear medicineMedicineChemistryPathologyRadiologyPhysicsMagnetic resonance imagingParkinson's diseaseDiseaseAstronomy

Abstract

fetched live from OpenAlex

Deep brain stimulation (DBS) surgery is performed on patients suffering Parkinson’s disease for whom medication is no longer effective in relieving their symptoms. The outcomes of the surgery are highly dependent on the placement accuracy at the targeted structure in the brain. We developed a DBS electrode that includes optical fibers to perform coherent anti-Stokes Raman scattering (CARS) spectroscopy and diffuse reflectance s pectroscopy ( DRS) d uring t he e lectrode i nsertion i n t he b rain. We w ere able to identify white and grey matter using principal component analysis (PCA), proving that spectroscopic measurements could be suitable for neuronavigation.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.063
GPT teacher head0.363
Teacher spread0.299 · 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
Published2023
Admission routes1
Has abstractyes

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