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Record W4414816710 · doi:10.1016/j.radonc.2025.111165

Outcomes and level of evidence in radiation therapy research and different categories of radiotherapy innovations: an ESTRO-VBRO bibliometrics analysis of the literature

2025· article· en· W4414816710 on OpenAlexaff
Miet Vandemaele, Grant Lewison, Hanneke Martinussen, Josep M. Borràs, Michelle Leech, M. Aznar, Pierre Blanchard, Yolande Lievens, Ajay Aggarwal

Bibliographic record

VenueRadiotherapy and Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsTrinity College
FundersUniversiteit Gent
KeywordsRadiation therapyReimbursementBibliometricsMEDLINECritical appraisal

Abstract

fetched live from OpenAlex

AIM: The ESTRO-Value-Based Radiation Oncology project aims to enhance patient access to high-value radiotherapy innovations, by identifying interventions delivering meaningful benefit. To understand the role of the quality of evidence in implementation decisions, this paper analyses the study designs and endpoints used to appraise selected types of radiotherapy innovations in the literature. METHODS: This review used a quantitative bibliometric approach to analyse a representative set of 23 radiotherapy innovations, identified within the radiation therapy research published between 2012 and 2022 in the Web of Science database. Abstracts were searched manually to extract information about study designs and endpoints. Interventions were allocated into one of four defined radiotherapy categories, based on a decision algorithm developed in a parallel project. RESULTS: 3,721 abstracts were identified and categorised using the decision algorithm into four categories: Drug-centred, Radiation-centred, Radiation-enabling or Operational radiotherapy interventions. The study designs were highly variable across these categories: in Drug-centred innovations, 20.3% were clinical trials compared to 6.8% for Radiation-centred. The predominant design across all categories was Prospective observational studies, ranging from 53.9% in Radiation-enabling to 23.0% in Drug-centred innovations. Regarding endpoints, the main focus for Drug-centred innovations was on Clinical endpoints and Overall survival. For Radiation-centred and Radiation-enabling innovations, Toxicity endpoints were more frequently reported. CONCLUSION: This analysis demonstrates the differences in radiotherapy research output for various categories of radiotherapy interventions. This supports the development of a tailored appraisal strategy for each category, based on the required level of evidence and meaningful endpoints to support reimbursement and clinical implementation.

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)0.093
metaresearch head score (Gemma)0.432
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score0.492

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0930.432
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.012
Bibliometrics0.2690.245
Science and technology studies0.0020.003
Scholarly communication0.0110.008
Open science0.0030.009
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.207
GPT teacher head0.509
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.

Study designObservational
DomainEvaluation
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

Citations4
Published2025
Admission routes1
Has abstractyes

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