MétaCan
Menu
Back to cohort
Record W2082983068 · doi:10.1002/rcs.308

New tactile sensing system for minimally invasive surgical tumour localization

2010· article· en· W2082983068 on OpenAlex

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.

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2010
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsLondon Health Sciences CentreWestern University
FundersEthicon Endo-Surgery
KeywordsEx vivoImaging phantomHaptic technologyBiomedical engineeringOccultInvasive surgeryComputer scienceIn vivoMedicineSurgeryNuclear medicinePathologyArtificial intelligenceBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Minimally invasive surgery (MIS) suffers from the inability to directly palpate organs for tumour localization. A tactile sensing system (TSS), consisting of a probe and a visualization interface, was developed to present an active pressure map of the contact surface to locate tumours during MIS. METHODS: The TSS performance was compared to MIS graspers to locate occult 10 mm phantom tumours in ex vivo bovine liver and ex vivo porcine lung. Performance assessment included applied pressure, localization distance and accuracy. RESULTS: The TSS realized a relative 71% reduction in maximum applied pressure and a 31% increase in detection accuracy in liver tissue (when compared to MIS graspers) and demonstrated no significant differences in performance when palpating lung tissue. CONCLUSIONS: The TSS may help surgeons to identify occult tumours during surgery by restoring some of the haptic information lost during MIS.

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 score0.920
Threshold uncertainty score0.438

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 head0.014
GPT teacher head0.249
Teacher spread0.235 · 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