Building an appraisal framework for radiotherapy innovations in a value-based context: The ESTRO-Value-based radiation oncology categorisation system
Bibliographic record
Abstract
AIM: There is no consistent appraisal strategy for radiotherapy innovations supporting their clinical implementation or regulatory decision-making, thus hampering access to high-value care. This study presents the development of a categorisation system as a first step towards a value-based appraisal framework for radiotherapy innovations within the ESTRO Value-Based Radiation Oncology (VBRO) project. METHODS: A mixed-method development process in four phases integrated qualitative and quantitative data in multiple rounds of revision, improvement and validation; and was supported by multidisciplinary stakeholders representing the European radiation oncology community. RESULTS: Four distinct categories of radiotherapy interventions are defined: Drug-centred, Radiation-centred, Radiation-enabling and Operational radiation interventions. Innovations are categorised based on their primary aim, focussing on either patient-level or organisational level; their technological characteristics; and their radiotherapy-specific characteristics such as therapeutic ratio, biological or dosimetric properties or radiotherapy-drug combinations. To support categorisation choices, a sequence of decision-making questions was arranged in a decision algorithm and presented as a decision tree. The categories and categorisation algorithm were validated using qualitative and quantitative methods by representative stakeholders of the European radiation oncology community, by a bibliometrical data analysis, and finally by the VBRO steering committee. CONCLUSION: A correct definition of the different radiotherapy categories is essential to study their interrelation with optimum study design, outcomes, and magnitude of benefit, in view of optimising evidence generation and tailored appraisals. This categorisation system forms the basis to create a value-based appraisal framework within the ESTRO-VBRO project, aimed to support implementation and authorisation regulations for each category of radiotherapy innovation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".