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Record W2473616517 · doi:10.1118/1.4958246

TH‐EF‐204‐06: Closing

2016· article· en· W2473616517 on OpenAlexaboutno aff
Cari Borrás

Bibliographic record

VenueMedical Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDosimetryRadiosurgeryMedical physicsStereotactic radiation therapyImage-guided radiation therapyField sizeLinear particle acceleratorNuclear medicineQuality assuranceInstrumentation (computer programming)Radiation therapyRadiation treatment planningMedicineComputer sciencePhysicsRadiologyBeam (structure)Optics

Abstract

fetched live from OpenAlex

Joanna E. Cygler, Jan Seuntjens, J. Daniel Bourland, M. Saiful Huq, Josep Puxeu Vaque, Daniel Zucca Aparicio, Tatiana Krylova, Yuri Kirpichev, Eric Ford, Caridad Borras Stereotactic Radiation Therapy (SRT) utilizes small static and dynamic (IMRT) fields, to successfully treat malignant and benign diseases using techniques such as Stereotactic Radiosurgery (SRS) and Stereotactic Body Radiation Therapy (SBRT). SRT is characterized by sharp dose gradients for individual fields and their resultant dose distributions. For appropriate targets, small field radiotherapy offers improved treatment quality by allowing better sparing of organs at risk while delivering the prescribed target dose. Specialized small field treatment delivery systems, such as robotic‐controlled linear accelerators, gamma radiosurgery units, and dynamic arc linear accelerators may utilize rigid fixation, image guidance, and tumor tracking, to insure precise dose delivery to static or moving targets. However, in addition to great advantages, small field delivery techniques present special technical challenges for dose calibration due to unique geometries and small field sizes not covered by existing reference dosimetry protocols such as AAPM TG‐51 or IAEA TRS 398. In recent years extensive research has been performed to understand small field dosimetry and measurement instrumentation. AAPM, IAEA and ICRU task groups are expected to provide soon recommendations on the dosimetry of small radiation fields. In this symposium we will: 1] discuss the physics, instrumentation, methodologies and challenges for small field radiation dose measurements; 2] review IAEA and ICRU recommendations on prescribing, recording and reporting of small field radiation therapy; 3] discuss selected clinical applications and technical aspects for specialized image‐guided, small field, linear accelerator based treatment techniques such as IMRT and SBRT. Learning Objectives: To learn the physics of small fields in contrast to dosimetry of conventional fields To learn about detectors suitable for small fields To learn about the role of Monte Carlo simulations in determination of small field output factors To provide an overview of the IAEA small field dosimetry recommendations To provide an overview of the content of the ICRU report on Prescribing, Reporting and Recording of Small Field Radiation Therapy. To learn about special technical considerations in delivering IMRT and SBRT treatments To appreciate specific challenges of IMRT implementation J. Seuntjens, Natural Sciences and Engineering Research Council; Canadian Institutes of Health Research

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.395
Threshold uncertainty score0.863

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0020.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.3950.316

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 head0.010
GPT teacher head0.288
Teacher spread0.278 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2016
Admission routes1
Has abstractyes

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