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Record W2009974839 · doi:10.1088/0031-9155/58/4/1075

Radiobiological effects of altering dose rate in filter-free photon beams

2013· article· en· W2009974839 on OpenAlexafffund
Tania Karan, Vitali Moiseenko, B.S. Gill, R Horwood, Alastair H. Kyle, A.I. Minchinton

Bibliographic record

VenuePhysics in Medicine and Biology · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsOccupational Cancer Research CentreBC Cancer Agency
FundersCanadian Institutes of Health Research
KeywordsTruebeamNuclear medicineRadiation therapyIrradiationLinear particle acceleratorFilter (signal processing)Dose rateCell survivalOpticsMedicineBeam (structure)Materials scienceChemistryAndrologyBiomedical engineeringPhysicsRadiochemistrySurgeryIn vitro

Abstract

fetched live from OpenAlex

To validate that altering radiotherapy dose rate through either changing pulse repetition frequency or instantaneous dose rate does not have an effect on cell survival, two human carcinoma and a hamster lung cell line were irradiated with various beam settings. Varian TrueBeam linac with a flattening filter free mode of operation was used for all experiments. The results obtained indicate that either method of changing dose rate has no effect on cell survival in the three cell lines studied. Filtered and filter free modes were also compared in treatments with protracted dose delivery which significantly increases overall treatment time. Cell survival indicated no difference between filter and filter free beam delivery in any of the protraction schemes. An increase in survival was seen in both modes upon protracting dose delivery to 15, 30 or 60 min rather than delivering acutely. Further, analysis of induced DNA double-strand breaks via the γH2AX assay showed no difference between filtered and unfiltered beams. The following study suggests that increasing dose rate is an acceptable manner of decreasing radiotherapy treatment time that does not have any detrimental effects on in vitro cell eradication.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.054
GPT teacher head0.349
Teacher spread0.295 · 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 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

Citations37
Published2013
Admission routes2
Has abstractyes

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