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Record W4251562541 · doi:10.32920/ryerson.14646285

A thermal dose controller for laser interstitial thermal therapy

2021· preprint· en· W4251562541 on OpenAlexaff
Madhu Jain

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsController (irrigation)ThermalLaserCascadeMaterials scienceControl theory (sociology)ObstacleBiomedical engineeringComputer scienceMedicineControl (management)EngineeringPhysicsOpticsChemical engineering

Abstract

fetched live from OpenAlex

Laser interstitial thermal therapy (LITT) is a minimally invasive technique for destroying localized solid tumors by heating with light. An obstacle to widespread adoption of LITT is the lack of adequate control of heating of surrounding healthy tissue and prevention of tissue and fiber-tip charring. An LITT thermal dose controller was developed to address these issues. The goal of the controller is to deliver prescribed thermal dose at a target location in tissue in a present treatment time. The developed feedback controller has a cascade structure with primary thermal dose control loop continuously generating the reference temperature for the secondary, constrained, model predictive temperature controller. The performance of controller was evaluated in simulated linear and non-linear tissue models and in albumen phantoms. The control system demonstrated the ability to achieve treatment goals across all evaluation models by delivering 240 eq. min dose at 5 mm in various preset treatment times.

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)0.001
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.298
Teacher spread0.282 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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