MétaCan
Menu
Back to cohort
Record W2142334243 · doi:10.1002/rcs.112

Detection of tumours using a computational tactile sensing approach

2006· article· en· W2142334243 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2006
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPalpationComputer scienceSoftwareArtificial intelligenceFinite element methodTactile sensorObject (grammar)Computer visionPattern recognition (psychology)Biomedical engineeringSurgeryMedicineStructural engineering

Abstract

fetched live from OpenAlex

BACKGROUND: A new method is presented for determining the existence of an embedded object in biological tissue. METHODS: We propose a method for modelling tissue containing a simulated tumour. Indications of the existence of a tumour that appear on the surface of the tissue were also determined. FEM provided properties such as the shape, size, depth and location of tumour. RESULTS: A number of different cases were created and solved by the software and tactile images and stress graphs were extracted. These results clearly showed the existence of the tumour in the tissue. Maximum stresses were used to create tactile maps. Simulation results demonstrated good agreements with other studies. CONCLUSIONS: Three-dimensional analysis leads to a novel method of predicting the characteristics of a tumour and can be directly applied to the incorporation of tactile sensing in artificial palpation, helping surgeons in non-invasive procedures.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.768
Threshold uncertainty score0.371

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 head0.019
GPT teacher head0.247
Teacher spread0.227 · 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