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Record W2090475718 · doi:10.1088/0031-9155/48/6/303

Theoretical modelling, experimental studies and clinical simulations of urethral cooling catheters for use during prostate thermal therapy

2003· article· en· W2090475718 on OpenAlexaff
Sean M. Davidson, Michael D. Sherar

Bibliographic record

VenuePhysics in Medicine and Biology · 2003
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsCatheterImaging phantomMaterials scienceCoolantBiomedical engineeringUrethraProstateHeat transfer coefficientMale urethraMedicineNuclear medicineHeat transferMechanicsSurgeryPhysicsThermodynamicsInternal medicine

Abstract

fetched live from OpenAlex

Urethral cooling catheters are used to prevent thermal damage to the urethra during thermal therapy of the prostate. Quantification of a catheter's heat transfer characteristics is necessary for prediction of the catheter's influence on the temperature and thermal dose distribution in periurethral tissue. Two cooling catheters with different designs were examined: the Dornier Urowave catheter and a prototype device from BSD Medical Corp. A convection coefficient, h. was used to characterize the cooling ability of each catheter. The value of the convection coefficient (h = 330 W m(-2) C(-1) for the Dornier catheter. h = 160 W m(-2) C(-1) for the BSD device) was obtained by comparing temperatures measured in a tissue-equivalent phantom material to temperatures predicted by a finite element method simulation of the phantom experiments. The coefficient was found to be insensitive to the rate of coolant flow inside the catheter between 40 and 120 ml min(-1). The convection coefficient method for modelling urethral catheters was incorporated into simulations of microwave heating of the prostate. Results from these simulations indicate that the Dornier device is significantly more effective than the BSD catheter at cooling the tissue surrounding the urethra.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.398

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 studies0.0000.001
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 head0.478
GPT teacher head0.528
Teacher spread0.050 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2003
Admission routes1
Has abstractyes

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