Peer Review in Radiation Oncology: Where Does the Middle East, North Africa, and Türkiye Region Stand?
Bibliographic record
Abstract
PURPOSE This study aims to assess the status of radiation oncology peer review procedures across the Middle East, North Africa, and Türkiye (MENAT) region. METHODS A cross-sectional electronic survey was conducted among radiotherapy centers in the MENAT region in March 2024. It assessed peer review practices, departmental demographics, perceived importance of peer review, and potential barriers. RESULTS Data from 177 radiation oncology centers revealed varying peer review implementation across the MENAT region. Egypt had the highest participation (16.4%) among all responders. Most centers (31%) treated 500-1,000 cases annually. The majority (77.4%) implemented peer review, with varying levels between countries and across different centers. Advanced radiotherapy techniques significantly correlated with implementation of peer review ( P < .05). Peer review meetings were mostly scheduled on a weekly basis (46%) and organized by radiation oncologists (84.7%). Target volume contouring (89%) and radiotherapy prescription (82%) were frequently peer-reviewed. Respondents with peer review at their institutions significantly valued peer review for education, adherence to guidelines, improving planning protocols, and reducing variation in practice institutions without peer review ( P < .05). The most frequently reported barriers to peer review were having a high number of patients (56%) and shortage of time (54%). CONCLUSION Peer review is essential for improving the quality of practice in radiation oncology. Despite some discrepancies, numerous obstacles, and challenges in implementation, it is instrumental in the improvement of patient care in most centers throughout the region. Raising awareness among radiation oncologists about the importance of peer review is paramount to lead to better outcomes.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".