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Record W2050050256 · doi:10.1088/0031-9155/53/9/r01

The physics, biophysics and technology of photodynamic therapy

2008· review· en· W2050050256 on OpenAlexaff
Brian C. Wilson, Michael S. Patterson

Bibliographic record

VenuePhysics in Medicine and Biology · 2008
Typereview
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsJuravinski Cancer CentreMcMaster UniversityUniversity of TorontoOntario Institute for Cancer Research
FundersNational Cancer Institute
KeywordsPhotodynamic therapyMedical physicsPhotosensitizerMedicineDosimetryNanotechnologyComputer sciencePhysicsMaterials scienceChemistryNuclear medicine

Abstract

fetched live from OpenAlex

Photodynamic therapy (PDT) uses light-activated drugs to treat diseases ranging from cancer to age-related macular degeneration and antibiotic-resistant infections. This paper reviews the current status of PDT with an emphasis on the contributions of physics, biophysics and technology, and the challenges remaining in the optimization and adoption of this treatment modality. A theme of the review is the complexity of PDT dosimetry due to the dynamic nature of the three essential components -- light, photosensitizer and oxygen. Considerable progress has been made in understanding the problem and in developing instruments to measure all three, so that optimization of individual PDT treatments is becoming a feasible target. The final section of the review introduces some new frontiers of research including low dose rate (metronomic) PDT, two-photon PDT, activatable PDT molecular beacons and nanoparticle-based PDT.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.199
GPT teacher head0.456
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations996
Published2008
Admission routes1
Has abstractyes

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