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Record W2588310132 · doi:10.1016/j.tipsro.2017.01.002

Risk management, quality and safety in ESTRO

2017· editorial· en· W2588310132 on OpenAlexaboutno aff
Mary Coffey

Bibliographic record

VenueTechnical Innovations & Patient Support in Radiation Oncology · 2017
Typeeditorial
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRadiation therapyQuality assuranceMedicineRadiation oncologyMedical physicsQuality (philosophy)Health careClinical OncologySurgical oncologyCancerOncologySurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

ESTRO has a long history in promoting the highest quality radiotherapy for all patients. This is clearly stated in their vision for radiation oncology and cancer care for the 2020 horizon “All patients are entitled to access healthcare systems that enable the highest quality radiotherapy with a safe healthcare environment……” and broken down in vision 1.1. “Optimised individualized patient care will be achieved by integrating new clinical and preclinical evidence from biology, functional and anatomic imaging, and the use of novel systemic agents together with the delivery of high-precision radiation therapy in a safety-aware environment.”1

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)0.055
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: Editorial · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.014
Scholarly communication0.0160.011
Open science0.0030.016
Research integrity0.0090.012
Insufficient payload (model declined to judge)0.0150.003

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 head0.018
GPT teacher head0.384
Teacher spread0.366 · 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
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

Citations4
Published2017
Admission routes1
Has abstractyes

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