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A review of small animal dosimetry techniques: image-guided and spatially fractionated therapy

2023· review· en· W4388698839 on OpenAlexaff
Christopher Johnstone, Magdalena Bazalova‐Carter

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typereview
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of VictoriaPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsDosimetryRadiation therapyMedical physicsImage-guided radiation therapyMedicineDosimeterNuclear medicineRadiology

Abstract

fetched live from OpenAlex

Abstract Research in small animal radiotherapy is a crucial step in clinical translation of novel radiotherapy techniques, either delivered as stand-alone treatment or in combination with other treatments, such as chemotherapy and immunotherapy. In order to efficiently translate preclinical findings to the clinical setting, preclinical radiotherapy must replicate clinical therapy in terms of mode of delivery as well as dose delivery accuracy as closely as possible. In this review article, we focused on the description of dosimetry tools for radiotherapy of small animals delivered with kilovoltage x-ray beams on image-guided irradiators and in a spatially-fractionated manner by means of microbeam therapy. The specifics of dosimetry of kilovoltage x-ray beam deliveries with small, often sub-millimeter, beams are highlighted, and suitable dosimeters, phantoms, and dose measurement and calculation techniques are reviewed. Future directions for accurate real-time high spatial resolution dosimetry of small animal irradiations are also discussed.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.069
GPT teacher head0.373
Teacher spread0.304 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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