MétaCan
Menu
Back to cohort
Record W2062329698 · doi:10.1088/0031-9155/45/5/303

Dynamic contrast enhanced CT measurement of blood flow during interstitial laser photocoagulation: comparison with an Arrhenius damage model

2000· article· en· W2062329698 on OpenAlexaff
Thomas G. Purdie, Ting-Yim Lee, Megumi Iizuka, Michael D. Sherar

Bibliographic record

VenuePhysics in Medicine and Biology · 2000
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsSt Joseph's Health CentreUniversity of TorontoOntario Institute for Cancer ResearchLawson Health Research InstituteWestern University
Fundersnot available
KeywordsArrhenius equationBlood flowMaterials scienceBiomedical engineeringNuclear medicineChemistryMedicineActivation energyRadiology

Abstract

fetched live from OpenAlex

One effect of heating during interstitial laser photocoagulation (ILP) is to directly destroy the tumour vasculature resulting in a loss of viable blood supply. Therefore, blood flow measured during and after treatment can be a useful indicator of tissue thermal damage. In this study, the effect of ILP treatment on rabbit thigh tumours was investigated by measuring blood flow changes using dynamic contrast enhanced computed tomography (CT). The CT measured changes in blood flow of treated tumour tissue were fitted to an Arrhenius model assuming first order rate kinetics. Our results show that changes in blood flow of tumour tissue distant from surrounding normal tissue are well described by an Arrhenius model. By contrast, the temperature profile of tumour tissue adjacent to normal tissue must be modified to account for heat dissipation by the latter. Finally, the Arrhenius parameters derived in the study are similar to those derived by heating tumour tissue to a lower temperature (<47 degrees C) than the current study. In conclusion, CT can be used to monitor blood flow changes during ILP and these measurements are related to the thermal damage predicted by the Arrhenius model.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.367

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 head0.085
GPT teacher head0.382
Teacher spread0.297 · 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 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

Citations19
Published2000
Admission routes1
Has abstractyes

Explore more

Same venuePhysics in Medicine and BiologySame topicOptical Imaging and Spectroscopy TechniquesFrench-language works237,207