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Record W2020108411 · doi:10.1177/153303460800700505

Surface Dose for Five Telecobalt Machines, 6MV Photon Beam from Four Linear Accelerators and a Hi-Art TomoTherapy

2008· article· en· W2020108411 on OpenAlexaboutno aff
Rajesh Kinhikar

Bibliographic record

VenueTechnology in Cancer Research & Treatment · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsnot available
FundersVarian Medical Systems
KeywordsTomotherapyLinear particle acceleratorThermoluminescent dosimeterNuclear medicineDosimeterRoentgenDosimetryIonization chamberField sizeThermoluminescent DosimetryBeam (structure)PhysicsMedical physicsOpticsMedicineRadiation therapyRadiology

Abstract

fetched live from OpenAlex

The purpose of this study was to estimate the surface dose for five telecobalt machines (four from Best Theratronics Limited, Canada, one from Panacea Medical Technologies, India), 6 MV photon beam (static) from four linear accelerators (three Varian linear accelerators and one Siemens) and Hi-Art Tomotherapy unit. The surface dose was measured with Thermoluminescent dosimeters in phantom slabs. For Tomotherapy 6 MV beam the surface dose was estimated as 32% while it was 35%, 33%, and 36% for Clinac 6EX, Clinac 2100CD, and Clinac 2100C linear accelerators, respectively. Similarly, the surface dose for 6 MV photon beam from Primus linear accelerator was estimated as 35%. Surface doses from telecobalt machines Equinox-80, Elite-80, Th-780C, Th-780, and Bhabhatron-II was found to be 30%, 29.1%, 27.8%, 29.3%, and 29.9% for 10 cm x 10 field size, respectively. Measured surface dose from all four linear accelerators were in good agreement with that of the Tomotherapy. The surface dose measurements were useful for Tomotherapy to predict the superficial dose during helical IMRT treatments.

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)
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 score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.062
GPT teacher head0.404
Teacher spread0.342 · 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 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

Citations9
Published2008
Admission routes1
Has abstractyes

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