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Record W2971309302 · doi:10.1109/pn.2019.8819600

iPDT for Glioblastoma Multiforme Management: Dosimetric Enhancements

2019· article· en· W2971309302 on OpenAlexaff
Daniel Molenhuis, Carl Fisher, Manjunatha Ankathatti Munegowda, Arkady Mandel, Fynn Schwiegelshohn, Vaughn Betz, Lothar Lilge

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsGlioblastomaPhotosensitizerPhotodynamic therapyMagnetic resonance imagingRadiation therapyMaterials scienceMedicineNuclear medicineBiomedical engineeringChemistryRadiologyCancer research

Abstract

fetched live from OpenAlex

Median survival for Glioblastoma patients remains at around 15 months after surgery, radiation, and chemotherapy but varies widely between surgeons and centres. Interstitial Photodynamic Therapy (iPDT) mediated by ALA induced PpIX or other photosensitizers is investigated at various clinics. To enable iPDT treatment planning, photosensitizer transport characteristics needs to be known in order to optimize photoactivation. A method using contrast enhanced, functional magnetic resonance imaging, compartment modelling, spatial frequency domain imaging (SFDI) and inductively coupled plasma mass spectrometry (ICP-MS) is proposed to predict and validate the localization characteristics of two photosensitizers in murine Glioblastoma models.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 head0.011
GPT teacher head0.317
Teacher spread0.306 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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