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Record W2087490690 · doi:10.1063/1.3125062

Three-dimensional fluence rate measurement and data acquisition system for minimally invasive light therapies

2009· article· en· W2087490690 on OpenAlexafffund
Benjamin Lai, Maxim Loshchenov, Alexander Douplik, Rob Rusnov, Marcos Jimenez-Davila, George Netchev, Lothar Lilge

Bibliographic record

VenueReview of Scientific Instruments · 2009
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsUniversity of Toronto
FundersNational Cancer InstituteCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsFluenceCalibrationData acquisitionDetectorOpticsContext (archaeology)Computer scienceMaterials scienceDosimetryPhysicsLaserNuclear medicine

Abstract

fetched live from OpenAlex

Light based therapies such as photodynamic therapy are in need of advanced tools for light fluence rate dosimetry and monitoring within the context of therapy planning and light delivery to ensure maximum treatment efficacy. The use of a single, multisensor fiber-based fluorescent probe capable of performing spatially resolved fluence rate measurements along an axis was demonstrated. This work extends the previous technique and describes a fluence rate quantification system able to employ up to 12 multisensor probes to simultaneously measure fluence rate distribution throughout a 3D treatment volume. The system optoelectronics provides for sensor calibration, data acquisition, and weighted least-squares processing to extract localized fluence rate information in real-time. Core components include an integrating cylinder for source sensor calibration, a 2D back thin CCD detector for sensor signal detection from multiple probes, high-speed data acquisition card, and custom software for real-time extraction of fluence rate information from all sensors.

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)0.003
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 score0.374
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.060
GPT teacher head0.332
Teacher spread0.272 · 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

Citations5
Published2009
Admission routes2
Has abstractyes

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