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Record W2005225437 · doi:10.1088/0031-9155/55/10/r01

RBE of low energy electrons and photons

2010· review· en· W2005225437 on OpenAlexfundno aff
Hooshang Nikjoo, L. Lindborg

Bibliographic record

VenuePhysics in Medicine and Biology · 2010
Typereview
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsRelative biological effectivenessIonizing radiationPhotonLinear energy transferRadiationRadiobiologyPhysicsElectronIrradiationChromosome aberrationPhoton energyAtomic physicsComputational physicsNuclear medicineNuclear physicsOpticsBiologyGeneticsChromosomeMedicine

Abstract

fetched live from OpenAlex

Relative biological effectiveness (RBE) compares the severity of damage induced by a radiation under test at a dose D relative to the reference radiation D(x) for the same biological endpoint. RBE is an important parameter in estimation of risk from exposure to ionizing radiation (IR). The present work provides a review of the recently published data and the knowledge of the RBE of low energy electrons and photons. The review presents RBE values derived from experimental data and model calculations including cell inactivation, chromosome aberration, cell transformation, micronuclei formation and induction of double-strand breaks. Biophysical models, including physical features of radiation track, and microdosimetry parameters are presented, analysed and compared with experimental data. The biological effects of low energy electrons and photons are of particular interest in radiation biology as these are strongly absorbed in micrometer and sub-micrometer layers of tissue. RBE values not only depend on the electron and photon energies but also on the irradiation condition, cell type and experimental conditions.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.163
GPT teacher head0.437
Teacher spread0.274 · 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

Citations189
Published2010
Admission routes1
Has abstractyes

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