Quality of radiotherapy services in post-Soviet countries: An IAEA survey
Bibliographic record
Abstract
BACKGROUND: The quality of radiotherapy services in post-Soviet countries has not yet been studied following a formal methodology. The IAEA conducted a survey using two sets of validated radiation oncology quality indicators (ROIs). METHODS: Eleven post-Soviet countries were assessed. A coordinator was designated for each country and acted as the liaison between the country and the IAEA. The methodology was a one-time cross-sectional survey using a 58-question tool in Russian. The questionnaire was based on two validated sets of ROIs: for radiotherapy centres, the indicators proposed by Cionini et al., and for data at the country level, the Australasian ROIs. RESULTS: The overall response ratio was 66.3%, but for the Russian Federation, it was 24%. Data were updated on radiotherapy infrastructure and equipment. 256 radiotherapy centres are operating 275 linear accelerators and 337 Cobalt-60 units. 61% of teletherapy machines are older than ten years. Analysis of ROIs revealed significant differences between these countries and radiotherapy practices in the West. Naming, task profile and education programmes of radiotherapy professionals are different than in the West. CONCLUSIONS: Most countries need modernization of their radiotherapy infrastructure coupled with adequate staffing numbers and updated education programmes focusing on evidence-based medicine, quality, and safety.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".