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Record W2790878980 · doi:10.1002/rcs.1893

An experimental and numerical study on tactile neuroimaging: A novel minimally invasive technique for intraoperative brain imaging

2018· article· en· W2790878980 on OpenAlex
Moslem Sadeghi‐Goughari, Yanjun Qian, Soo Jeon, Sohrab Sadeghi, Hyock‐Ju Kwon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2018
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNeuroimagingNeurosurgeryMedicineRadiologyNeuroscienceComputer sciencePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: The success of tumour neurosurgery is highly dependent on the ability to accurately localize the operative target, which may shift during the operation. Performing intraoperative brain imaging is crucial in minimally invasive neurosurgery to detect the effect of brain shift on the tumour's location, and to maximize the efficiency of tumour resection. METHOD: The major objective of this research is to introduce tactile neuroimaging as a novel minimally invasive technique for intraoperative brain imaging. To investigate the feasibility of the proposed method, an experimental and numerical study was first performed on silicone phantoms mimicking the brain tissue with a tumour. Then the study was extended to a clinical model with the meningioma tumour. RESULTS: The stress distribution on the brain surface has high potential to intraoperatively localize the tumour. CONCLUSION: Results suggest that tactile neuroimaging can be used to provide non-invasive and real-time intraoperative data on a tumour's features.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.491

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 head0.026
GPT teacher head0.313
Teacher spread0.286 · 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