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Record W4392859627 · doi:10.1016/j.tipsro.2024.100243

The Radiation Therapist profession through the lens of new technology: A practice development paper based on the ESTRO Radiation Therapist Workshops

2024· article· en· W4392859627 on OpenAlexaff
Michelle Leech, Alaa Abdalqader, S. Alexander, Nigel Anderson, B. Barbosa, Dylan Callens, Victoria Chapman, Mary Coffey, Maya Cox, Ilija Curic, J.Atlee Dean, Elizabeth Denney, Maeve Kearney, Vincent W.S. Leung, Martina Mortsiefer, E. Nirgianaki, Justas Povilaitis, Dimitra Strikou, Kenton Thompson, Maud van den Bosch, Michael Velec, Katrina Woodford, Monica Buijs

Bibliographic record

VenueTechnical Innovations & Patient Support in Radiation Oncology · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsRadiation TherapistMedicineMedical physicsLens (geology)OptometrySurgeryRadiation therapyEngineering

Abstract

fetched live from OpenAlex

Technological advances in radiation therapy impact on the role and scope of practice of the radiation therapist. The European Society of Radiotherapy and Oncology (ESTRO) recently held two workshops on this topic and this position paper reflects the outcome of this workshop, which included radiation therapists from all global regions. Workflows, quality assurance, research, IGRT and ART as well as clinical decision making are the areas of radiation therapist practice that will be highly influenced by advancing technology in the near future. This position paper captures the opportunities that this will bring to the radiation therapist profession, to the practice of radiation therapy and ultimately to patient care.

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)0.037
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.006
Scholarly communication0.0070.008
Open science0.0030.014
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0050.002

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 head0.022
GPT teacher head0.357
Teacher spread0.335 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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