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Record W2069451956 · doi:10.1177/153303460800700405

Photodynamic Therapy for Treatment of Solid Tumors — Potential and Technical Challenges

2008· review· en· W2069451956 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTechnology in Cancer Research & Treatment · 2008
Typereview
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsThunder Bay Regional Health Sciences Centre
FundersNational Cancer InstituteNational Institutes of Health
KeywordsPhotodynamic therapyPhotosensitizerMedicineMedical physicsChemistryPhotochemistry

Abstract

fetched live from OpenAlex

Photodynamic therapy (PDT) involves the administration of photosensitizer followed by local illumination with visible light of specific wavelength(s). In the presence of oxygen molecules, the light illumination of photosensitizer can lead to a series of photochemical reactions and consequently the generation of cytotoxic species. The quantity and location of PDT-induced cytotoxic species determine the nature and consequence of PDT. Much progress has been seen in both basic research and clinical application in recent years. Although the majority of approved PDT clinical protocols have primarily been used for the treatment of superficial lesions of both malignant and non-malignant diseases, interstitial PDT for the ablation of deep-seated solid tumors are now being investigated worldwide. The complexity of the geometry and non-homogeneity of solid tumor pose a great challenge on the implementation of minimally invasive interstitial PDT and the estimation of PDT dosimetry. This review will discuss the recent progress and technical challenges of various forms of interstitial PDT for the treatment of parenchymal and/or stromal tissues of solid tumors.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0030.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.149
GPT teacher head0.496
Teacher spread0.347 · 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