A prospective audit of thoracic reirradiation practice and peer-review in a high-volume academic center
Bibliographic record
Abstract
Background: Reirradiation is an increasingly common challenge with limited prospective evidence to guide practice, which varies internationally. This paper presents the patterns of practice in thoracic reirradiation within a high-volume academic center. Methods: Thoracic reirradiation cases, discussed at the thoracic radiotherapy quality assurance (QA) meeting, were prospectively collected over 12 months between April 2024 and March 2025. Data collected included patient demographics, primary tumor site, details of previous and current planned radiotherapy, the extent and type of overlap and any treatment plan modifications. The data was analyzed using descriptive statistics. Results: 85 (18.2 % of 466 cases) reirradiation cases were identified at 26 QA meetings. Most reirradiation plans (68.2 %) were of radical intent, with dose overlap (89.4 %, n = 76). Challenges included unreliable registration of prior radiotherapy datasets (16.5 %) and deciding appropriate plan modifications to improve safety: 24.7 % optimized dose distribution to an OAR, 23.5 % involved dose reductions from standard prescriptions and 15.3 % compromised target volume coverage. The most frequently identified dose-limiting OARs were the proximal bronchial tree, esophagus, and spinal cord. Concerns about a lack of normal tissue recovery arose in 7.1 % of cases. In 10.6 % of cases there was explicit discussion of a dose discount for OARs for presumed partial tissue recovery. Peer-review prompted revision of the treatment plan in 11.8 % of cases. Conclusion: These findings underscore the complexity of thoracic reirradiation and highlight the need for further guidance in the area and the role of QA rounds in optimizing safety and treatment decisions while best practice remains uncertain.
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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 | 0.044 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".