A systematic literature review of definitions and classification systems for radiotherapy innovation: A first step towards building a value-based assessment tool for radiation oncology
Bibliographic record
Abstract
INTRODUCTION: Timely access to radiotherapy innovations remains suboptimal, partly because there is no commonly agreed appraisal system suitable for the broad range of radiotherapy interventions. The Health Economics in Radiation Oncology (HERO) programme of ESTRO therefore engaged in building a radiotherapy-specific value-based framework. We report on a first step towards that aim, documenting the available definitions and classification systems for radiotherapy interventions. METHODS: A systematic literature search was carried out in Pubmed and Embase, following PRISMA methodology and using search terms on 'innovation', 'radiotherapy', 'definition' and 'classification'. Data were extracted from articles that met prespecified inclusion criteria. RESULTS: Out of 13,353 articles, 25 met the inclusion criteria, resulting in the identification of 7 definitions of innovation and 15 classification systems applicable to radiation oncology. Iterative appraisal divided the classification systems into two groups. A first group of 11 systems categorized innovations according to the perceived magnitude of innovation, typically 'minor' versus 'major'. The remaining 4 systems categorised innovations according to radiotherapy-specific characteristics, such as the type of radiation equipment or radiobiological properties. Here, commonly used terms as 'technique' or 'treatment' were found to be used in different meanings. DISCUSSION: There is no widely accepted definition or classification system for radiotherapy innovations. The data however suggest that unique properties of radiotherapy interventions can be used to categorise innovations in radiation oncology. Still, there remains a need for clear terminology denoting radiotherapy-specific characteristics. CONCLUSION: Building on this review, the ESTRO-HERO project will define what is required for a radiotherapy-specific value-based assessment tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".