Radiotherapy staffing in the European countries: Final results from the ESTRO-HERO survey
Bibliographic record
Abstract
BACKGROUND: The ESTRO Health Economics in Radiation Oncology (HERO) project has the overall aim to develop a knowledge base of the provision of radiotherapy in Europe and build a model for health economic evaluation of radiation treatments at the European level. The first milestone was to assess the availability of radiotherapy resources within Europe. This paper presents the personnel data collected in the ESTRO HERO database. MATERIALS AND METHODS: An 84-item questionnaire was sent out to European countries, through their national scientific and professional radiotherapy societies. The current report includes a detailed analysis of radiotherapy staffing (questionnaire items 47-60), analysed in relation to the annual number of treatment courses and the socio-economic status of the countries. The analysis was conducted between February and July 2014, and is based on validated responses from 24 of the 40 European countries defined by the European Cancer Observatory (ECO). RESULTS: A large variation between countries was found for most parameters studied. Averages and ranges for personnel numbers per million inhabitants are 12.8 (2.5-30.9) for radiation oncologists, 7.6 (0-19.7) for medical physicists, 3.5 (0-12.6) for dosimetrists, 26.6 (1.9-78) for RTTs and 14.8 (0.4-61.0) for radiotherapy nurses. The combined average for physicists and dosimetrists is 9.8 per million inhabitants and 36.9 for RTT and nurses. Radiation oncologists on average treat 208.9 courses per year (range: 99.9-348.8), physicists and dosimetrists conjointly treat 303.3 courses (range: 85-757.7) and RTT and nurses 76.8 (range: 25.7-156.8). In countries with higher GNI per capita, all personnel categories treat fewer courses per annum than in less affluent countries. This relationship is most evident for RTTs and nurses. Different clusters of countries can be distinguished on the basis of available personnel resources and socio-economic status. CONCLUSIONS: The average personnel figures in Europe are now consistent with, or even more favourable than the QUARTS recommendations, probably reflecting a combination of better availability as such, in parallel with the current use of more complex treatments than a decade ago. A considerable variation in available personnel and delivered courses per year however persists among the highest and lowest staffing levels. This not only reflects the variation in cancer incidence and socio-economic determinants, but also the stage in technology adoption along with treatment complexity and the different professional roles and responsibilities within each country. Our data underpin the need for accurate prediction models and long-term education and training programmes.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".