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Record W4403561821 · doi:10.1016/j.tipsro.2024.100281

Best practice in radiation oncology: A project to train the trainers: Review of 2008–2023

2024· article· en· W4403561821 on OpenAlexaff
Mary Coffey, Colleen Dickie, Elena Fidarova, Velimir Karadža, Philipp Scherer, Michelle Leech

Bibliographic record

VenueTechnical Innovations & Patient Support in Radiation Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsRadiation oncologySurgical oncologyMedicineMedical physicsRadiation therapyMedical educationGeneral surgeryOncologySurgery

Abstract

fetched live from OpenAlex

The European Society of Radiotherapy and Oncology (ESTRO) and the International Atomic Energy Agency (IAEA) project on 'best practice in radiation oncology: a project to train the radiation therapist trainers' commenced in 2008. The aim of the project was to influence education programmes throughout, mainly, eastern Europe to increase the radiation therapy-specific education of their programmes. This is to enable graduates of these programmes to be radiation therapists that are fit for purpose in the clinic. This paper provides an update on the successes of the project to date, the challenges that remain and the new approaches to project delivery by the faculty over this period.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.473
Teacher spread0.434 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2024
Admission routes1
Has abstractyes

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