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Record W1976509908 · doi:10.1007/s11999-008-0678-5

Treatment of Canine Osseous Tumors with Photodynamic Therapy: A Pilot Study

2009· article· en· W1976509908 on OpenAlexafffund
Shane Burch, Cheryl A. London, Bernard Séguin, Carlos O. Rodriguez, Brian C. Wilson, Stuart K. Bisland

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2009
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsUniversity of Toronto
FundersUniversity of California, San FranciscoUniversity of Toronto
KeywordsMedicinePhotodynamic therapyNecrosisPathologyMagnetic resonance imagingNuclear medicineRadiology

Abstract

fetched live from OpenAlex

Photodynamic therapy uses nonthermal coherent light delivered via fiber optic cable to locally activate a photosensitive chemotherapeutic agent that ablates tumor tissue. Owing to the limitations of light penetration, it is unknown whether photodynamic therapy can treat large osseous tumors. We determined whether photodynamic therapy can induce necrosis in large osseous tumors, and if so, to quantify the volume of treated tissue. In a pilot study we treated seven dogs with spontaneous osteosarcomas of the distal radius. Tumors were imaged with MRI before and 48 hours after treatment, and the volumes of hypointense regions were compared. The treated limbs were amputated immediately after imaging at 48 hours and sectioned corresponding to the MR axial images. We identified tumor necrosis histologically; the regions of necrosis corresponded anatomically to hypointense tissue on MRI. The mean volume of necrotic tissue seen on MRI after photodynamic therapy was 21,305 mm(3) compared with a pretreatment volume of 6108 mm(3). These pilot data suggest photodynamic therapy penetrates relatively large canine osseous tumors and may be a useful adjunct for treatment of bone 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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.100
GPT teacher head0.444
Teacher spread0.344 · 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

Citations23
Published2009
Admission routes2
Has abstractyes

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