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Record W2018736466 · doi:10.1118/1.3592021

Surface dose measurement for helical tomotherapy

2011· article· en· W2018736466 on OpenAlexaff
Jonatan Snir, Homeira Mosalaei, Kevin Jordan, Slav Yartsev

Bibliographic record

VenueMedical Physics · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsDosimeterTomotherapyDosimetryOptically stimulated luminescenceMaterials scienceImaging phantomDiodeOpticsDose profileMOSFETOptoelectronicsNuclear medicineTransistorLuminescencePhysicsMedicineRadiation therapy

Abstract

fetched live from OpenAlex

PURPOSE: To compare the surface dose measurements made by different dosimeters for the helical tomotherapy (HT) plan in the case of the target close to the surface. METHODS: Surface dose measurements in different points for the HT plan to deliver 2 Gy to the planning target volume (PTV) at 5 mm below the surface of the cylindrical phantom were performed by radiochromic films, single use metal oxide semiconductor field-effect transistor (MOSFET) dosimeters, silicon IVD QED diode, and optically stimulated luminescence (OSL) dosimeters. RESULTS: The measured doses by all dosimeters were within 12 +/- 8% difference of each other. CONCLUSIONS: Radiochromic films, EBT, and EBT2, provide high spatial resolution, although it is difficult to get accurate measurements of dose. Both the OSL and QED measured similar dose to that of the MOSFET detectors. The QED dosimeter is promising as a reusable on-line wireless dosimeter, while the OSL dosimeters are easier to use, require minimum setup time and are very precise.

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)0.000
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.052
GPT teacher head0.309
Teacher spread0.257 · 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

Citations10
Published2011
Admission routes1
Has abstractyes

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