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Record W2912147078 · doi:10.3390/cancers11020170

Integrating Small Animal Irradiators with Functional Imaging for Advanced Preclinical Radiotherapy Research

2019· review· en· W2912147078 on OpenAlexfundno aff
Mihaela Ghita, Kathryn H. Brown, Olivia J. Kelada, Edward E. Graves, Karl T. Butterworth

Bibliographic record

VenueCancers · 2019
Typereview
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsnot available
FundersQueen's UniversityQueen's University BelfastNational Centre for the Replacement, Refinement and Reduction of Animals in Research
KeywordsPreclinical researchTranslational researchMedical physicsRadiation therapyMedicineMolecular imagingAnimal modelRadiation oncologyIn vivoPathologyRadiologyBiologyInternal medicineBiotechnology

Abstract

fetched live from OpenAlex

Translational research aims to provide direct support for advancing novel treatment approaches in oncology towards improving patient outcomes. Preclinical studies have a central role in this process and the ability to accurately model biological and physical aspects of the clinical scenario in radiation oncology is critical to translational success. The use of small animal irradiators with disease relevant mouse models and advanced in vivo imaging approaches offers unique possibilities to interrogate the radiotherapy response of tumors and normal tissues with high potential to translate to improvements in clinical outcomes. The present review highlights the current technology and applications of small animal irradiators, and explores how these can be combined with molecular and functional imaging in advanced preclinical radiotherapy research.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.252
GPT teacher head0.509
Teacher spread0.257 · 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 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

Citations26
Published2019
Admission routes1
Has abstractyes

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