MétaCan
Menu
Back to cohort
Record W2140951172 · doi:10.1088/0031-9155/49/14/014

Transperineal<i>in vivo</i>fluence-rate dosimetry in the canine prostate during SnET2-mediated PDT

2004· article· en· W2140951172 on OpenAlexaff
Lothar Lilge, Natalie Pomerleau‐Dalcourt, Alexander Douplik, Steven H. Selman, Rick W. Keck, Maria Szkudlarek, Maciej Pestka, Jerzy Jankun

Bibliographic record

VenuePhysics in Medicine and Biology · 2004
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoOntario Institute for Cancer Research
FundersNational Cancer Institute
KeywordsDosimetryProstatePhotodynamic therapyFluenceAttenuationBiomedical engineeringMaterials scienceProstate cancerPhotosensitizerOpticsNuclear medicineMedicineChemistryCancerPhysicsLaserInternal medicine

Abstract

fetched live from OpenAlex

Advances in photodynamic therapy (PDT) treatment for prostate cancer can be achieved either by improving selectivity of the photosensitizer towards prostate gland tissue or improving the dosimetry by means of individualized treatment planning using currently available photosensitizers. The latter approach requires the ability to measure, among other parameters, the fluence rate at different positions within the prostate and the ability to derive the tissue optical properties. Here fibre optic probes are presented capable of measuring the fluence rate throughout large tissue volumes and a method to derive the tissue optical properties for different volumes of the prostate. The responsivity of the sensors is sufficient to detect a fluence rate of 0.1 mW cm(-2). The effective attenuation coefficient in the canine prostate at 660 nm is higher at the capsule (2.15+/-0.19 cm(-1)) than in proximity of the urethra (1.84+/-0.36 cm(-1)). Significant spatial and temporal intra- and inter-canine variability in the tissue optical properties was noted, highlighting the need for individualized monitoring of the fluence rate for improved dosimetry.

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)0.001
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 score0.839
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.084
GPT teacher head0.383
Teacher spread0.299 · 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

Citations22
Published2004
Admission routes1
Has abstractyes

Explore more

Same venuePhysics in Medicine and BiologySame topicPhotodynamic Therapy Research StudiesFrench-language works237,207