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Record W4225093337 · doi:10.1039/d2lc00177b

Radiotherapy on-chip: microfluidics for translational radiation oncology

2022· review· en· W4225093337 on OpenAlexafffund
Rodin Chermat, Maryam Ziaee, David Y. Mak, Elena Refet-Mollof, Françis Rodier, Philip Wong, Jean‐François Carrier, Yuji Kamio, Thomas Gervais

Bibliographic record

VenueLab on a Chip · 2022
Typereview
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversité de MontréalPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of TorontoPolytechnique MontréalCentre Hospitalier de l’Université de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsRadiation therapyMicrofluidicsRadiation oncologyMedical physicsRadiobiologyTranslational researchNanotechnologyMedicineComputer scienceEngineering ethicsEngineeringInternal medicinePathologyMaterials science

Abstract

fetched live from OpenAlex

The clinical importance of radiotherapy in the treatment of cancer patients justifies the development and use of research tools at the fundamental, pre-clinical, and ultimately clinical levels, to investigate their toxicities and synergies with systemic agents on relevant biological samples. Although microfluidics has prompted a paradigm shift in drug discovery in the past two decades, it appears to have yet to translate to radiotherapy research. However, the materials, dimensions, design versatility and multiplexing capabilities of microfluidic devices make them well-suited to a variety of studies involving radiation physics, radiobiology and radiotherapy. This review will present the state-of-the-art applications of microfluidics in these fields and specifically highlight the perspectives offered by radiotherapy on-a-chip in the field of translational radiobiology and precision medicine. This body of knowledge can serve both the microfluidics and radiotherapy communities by identifying potential collaboration avenues to improve patient care.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0020.000

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 head0.039
GPT teacher head0.373
Teacher spread0.334 · 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 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

Citations16
Published2022
Admission routes2
Has abstractyes

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