Board-certified specialty training program in radiation oncology in a war-torn country: Challenges, solutions and outcomes
Bibliographic record
Abstract
BACKGROUND: Residency programs leading to board certification are important for safe and competent Radiation Oncology (RO) practice. In some developing nations, there is a gap in this field. This work addresses the experience that was accomplished to establish such a program in Iraq despite all the challenges that faces a country under war. METHODS: Descriptive report of challenges faced in a developing country that is still reeling from war, the steps taken to overcome these challenges and outcomes after graduation of two classes. RESULTS: After over 18 months of prerequisite technical and logistical preparations, a group of local and external faculty members were invited to establish the required syllabus of a structured RO residency program in Iraq. It is comprised of a total of 100 post-graduate academic credits over a 48-months period after clinical internship. First year evaluations included regular practical assessments; seven in-house papers covering RO, cancer and radiation biology, medical physics, radiological anatomy and diagnostic oncology, tumor pathology, onco-pharmacology, and medical statistics, research methodology, and cancer epidemiology, followed by a comprehensive examination. Subsequent evaluations were on an annual bases with enrollment in the American College of Radiology In-Training examination in RO. Final assessment included logbook and skills' reviews, graduation thesis or peer-review publication, two-papers' written examination, and an exit practical examination. CONCLUSIONS: residency program leading to board certification in RO was successfully started in Iraq. The new specialists will help in addressing the shortage of radiation oncologists in the country.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".