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Record W4406480614 · doi:10.1002/mp.17613

AAPM Task Group Report 274: Fluence rate dosimetry for photodynamic therapy (PDT)

2025· review· en· W4406480614 on OpenAlexaff
Timothy C. Zhu, Brian W. Pogue, Andreea Dimofte, Jarod C. Finlay, Lothar Lilge, Ulaş Sunar, Charles B Simone, R.L.P. van Veen

Bibliographic record

VenueMedical Physics · 2025
Typereview
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of Health
KeywordsDosimetryPhotodynamic therapyTask groupMedical physicsNuclear medicineMedicineChemistryEngineering

Abstract

fetched live from OpenAlex

Photodynamic therapy (PDT) is a treatment modality clinically approved for several oncologic indications, including esophageal and endobronchial cancers, precancerous conditions including Barrett's esophagus and actinic keratosis, and benign conditions like age-related macular degeneration. While it is currently clinically underused, PDT is an area of significant research interest. Because PDT relies on the absorption of light energy by intrinsic or administered absorbers, the dosimetric quantity of interest is the absorbed energy per unit mass of tissue, proportional to the fluence rate of light in tissue. It has been demonstrated that the fluence rate at the tissue surface may differ significantly from the incident irradiance of light because of multiple scattering and absorption, both of which may vary among patients and tissue types. This report will review the current state-of-the-art fluence rate dosimetry technology. It will describe the two types of detectors currently available for fluence rate measurement, scattering-tip and fluorescence-based detectors, and review their principles of operation. The report will recommend strategies to establish calibration and quality assurance procedures for clinical fluence rate dosimetry equipment, and it will establish guidelines for clinical implementation of fluence rate 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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.035
GPT teacher head0.398
Teacher spread0.364 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2025
Admission routes1
Has abstractyes

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