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Record W4399305769 · doi:10.1088/1361-6560/ad539d

Metrology for advanced radiotherapy using particle beams with ultra-high dose rates

2024· article· en· W4399305769 on OpenAlexaff
Anna Subiel, Alexandra Bourgouin, Rafael Kranzer, Peter Peier, Franziska Frei, F. Gómez, A. Knyziak, C. Fleta, Claude Bailat, Andreas Schüller

Bibliographic record

VenuePhysics in Medicine and Biology · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsNational Research Council Canada
FundersEuropean Metrology Programme for Innovation and ResearchNational Physical LaboratoryEuropean Commission
KeywordsDosimetryMedical physicsRadiation therapyMetrologyFlash (photography)Computer scienceMedical physicistMedicineSystems engineeringNuclear medicineEngineeringPhysicsRadiologyOptics

Abstract

fetched live from OpenAlex

Dosimetry of ultra-high dose rate beams is one of the critical components which is required for safe implementation of FLASH radiotherapy (RT) into clinical practice. In the past years several national and international programmes have emerged with the aim to address some of the needs that are required for translation of this modality to clinics. These involve the establishment of dosimetry standards as well as the validation of protocols and dosimetry procedures. This review provides an overview of recent developments in the field of dosimetry for FLASH RT, with particular focus on primary and secondary standard instruments, and provides a brief outlook on the future work which is required to enable clinical implementation of FLASH RT.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.003

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 head0.072
GPT teacher head0.404
Teacher spread0.332 · 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 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

Citations15
Published2024
Admission routes1
Has abstractyes

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