Improving the quality of radiation oncology: 10 years’ experience of QUATRO audits in the IAEA Europe Region
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The IAEA has developed a methodology for comprehensive quality audits of radiotherapy practices called Quality Assurance Team for Radiation Oncology (QUATRO). This study explores the factors that impacted quality of care among QUATRO audited centres in the IAEA Europe Region. MATERIALS AND METHODS: The 31 QUATRO reports collected over 10years include extensive data describing the quality of radiotherapy at the audited centres. A coding key was developed to aggregate and review these data in terms of recommendations for improvement and positive findings (commendations). RESULTS: Overall 759 recommendations and 600 commendations were given. Eight centres recognized as centres of competence differed from other centres mostly because they operated complete quality management systems and were adequately staffed. Other centres had excessive staff workloads and many gaps in the process of care. Insufficient equipment levels were prevalent. Patient centredness, communication, dosimetry, quality control and radiation protection were frequently commended by QUATRO. CONCLUSIONS: This analysis points to barriers to quality care such as insufficient staffing, education/training, equipment and lack of quality management. It highlights the correlation between the human resources availability and quality of care. It has also identified common action items for enhancing quality of radiotherapy programmes in the Region.
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 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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".