MétaCan
Menu
Back to cohort
Record W2520178700 · doi:10.1016/j.addr.2016.09.002

Advancements in brachytherapy

2016· review· en· W2520178700 on OpenAlex
Kari Tanderup, Cynthia Ménard, Csaba Polgár, Jacob Christian Lindegaard, Christian Kirisits, Richard Pötter

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

VenueAdvanced Drug Delivery Reviews · 2016
Typereview
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsCentre Hospitalier de l’Université de MontréalPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsBrachytherapyMedicineExternal beam radiotherapyRadiation therapyMedical physicsRadiologyBoosting (machine learning)DosimetryNuclear medicineComputer science

Abstract

fetched live from OpenAlex

Brachytherapy is a radiotherapy modality associated with a highly focal dose distribution. Brachytherapy treats the cancer tissue from the inside, and the radiation does not travel through healthy tissue to reach the target as with external beam radiotherapy techniques. The nature of brachytherapy makes it attractive for boosting limited size target volumes to very high doses while sparing normal tissues. Significant developments over the last decades have increased the use of 3D image guided procedures with the utilization of CT, MRI, US and PET. This has taken brachytherapy to a new level in terms of controlling dose and demonstrating excellent clinical outcome. Interests in focal, hypofractionated and adaptive treatments are increasing, and brachytherapy has significant potential to develop further in these directions with current and new treatment indications.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.358
Teacher spread0.330 · 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