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Record W3030886033 · doi:10.1145/3387168.3389110

Effects of Heterogeneous Surroundings on the Efficacy of Continuous Radiofrequency for Pain Relief

2019· article· en· W3030886033 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

VenueProceedings of the 3rd International Conference on Vision, Image and Signal Processing · 2019
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPain reliefMedicineComputer scienceAnesthesia

Abstract

fetched live from OpenAlex

This numerical study highlights the deviation between the predicted lesion volume of the homogeneous and heterogeneous models of the continuous radiofrequency (RF) procedure for pain relief. A three-dimensional computational domain comprising of a realistic anatomy of the target tissue has been considered in the present study. A comparative analysis has been conducted for three different scenarios: (a) completely homogenous domain comprising of only muscle tissue, (b) heterogeneous domain comprising of nerve and muscle tissues, and (c) heterogeneous domain comprising of bone, nerve and muscle tissues. Finite-element-based simulations have been performed for computing the temperature and electrical field distributions during the continuous RF procedures for treating chronic pain. The predicted results reveal that the consideration of heterogeneity within the computational domain results in distorted electric field distribution and leads to the significant reduction in the attained lesion volume during the continuous RF application for pain relief.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.362

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 head0.009
GPT teacher head0.239
Teacher spread0.229 · 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