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Record W2068616203 · doi:10.1118/1.1861413

Dosimetric effect of collimating jaws for small multileaf collimated fields

2005· article· en· W2068616203 on OpenAlexaff
James C. L. Chow, Melanie Seguin, Andrew Alexander

Bibliographic record

VenueMedical Physics · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsGrand River Hospital
Fundersnot available
KeywordsCollimated lightDosimetryMultileaf collimatorOpticsMedical physicsPhysicsNuclear medicineLinear particle acceleratorMedicineBeam (structure)Laser

Abstract

fetched live from OpenAlex

The dosimetric effects from the jaw positioned close to the small field (0.5 x 0.5, 1 x 1, and 2 x 2 cm2) side-edge generated by a single-focused multileaf collimator (MLC) were measured and studied. The measurement is important in intensity modulated radiotherapy (IMRT) because generally the jaw cannot perfectly cover all the leaf-ends in a segment of irregular field. This leads to additional dose contributed by (1) the end surface of the jaw, (2) the leaf-end, and (3) the inter- and intraleaf leakage/transmissions during the dosimetric measurement. Moreover, most of the conventional treatment planning systems ignore these effects in the dose calculation. In this study, measurements were made using a Varian 21 EX linear accelerator with 6 MV photon beam through a MLC containing 120 leaves. Percentage depth dose, beam profile, and output for small fields were measured by varying the jaw at different positions away from the leaf-ends in the field side-edge. Moving the jaw away from the leaf-ends increases the output and penumbra width for the small fields. Such increase is particularly significant when the field size is small (0.5 x 0.5 cm2) and the degree of increase changes quickly when the jaw-end is at about 1-2 cm from the leaf-end. It is suggested that measurements should be carried out in the IMRT commissioning to provide information to physicists in reviewing the treatment planning system's accuracy regarding leaf leakage/transmission and jaw effects.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.534

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 head0.011
GPT teacher head0.299
Teacher spread0.289 · 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 designOther design
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

Citations46
Published2005
Admission routes1
Has abstractyes

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