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Record W2512565858 · doi:10.1118/1.4961772

Sci‐Thur PM – Brachytherapy 04: Commissioning and Implementation of a Cobalt‐60 High Dose Rate Brachytherapy Source

2016· article· en· W2512565858 on OpenAlexaff
Jonathan Dysart

Bibliographic record

VenueMedical Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsHorizon Health Network
Fundersnot available
KeywordsBrachytherapyCobalt-60Dose rateWorkloadNuclear medicineCobaltDosimetryMedical physicsElectromagnetic shieldingMedicineMaterials scienceComputer sciencePhysicsRadiation therapyRadiologyOperating systemNuclear physics

Abstract

fetched live from OpenAlex

An Eckert & Ziegler Bebig Co0.A86 cobalt 60 high dose rate (HDR) brachytherapy source was commissioned for clinical use. Long‐lived Co‐60 HDR sources offer potential logistical and economic advantages over Ir‐192 sources, and should be considered for low to medium workload brachytherapy departments where modest increases in treatment times are not a factor. In optimized plans, the Co‐60 source provides a similar dose distribution to Ir‐192 despite the difference in radiation energy. By switching to Co‐60, source exchange frequency can be reduced by a factor of 20, resulting in overall financial savings of more than 50% compared to Ir‐192 sources. In addition, a reduction in Physicist QA workload of roughly 200 hours over the 5 year life of the Co‐60 source is also expected. These benefits should be considered against the modest increases in average treatment time compared to those of Ir‐192 sources, as well as the centre‐specific needs for operating room shielding modification.

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)0.002
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 score0.009
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.009
GPT teacher head0.311
Teacher spread0.302 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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