Enhancing value of quality assurance rounds in improving radiotherapy management: a retrospective analysis from King Hussein Cancer Center in Jordan
Bibliographic record
Abstract
PURPOSE: The quality assurance (QA) chart rounds are multidisciplinary meetings to review radiation therapy (RT) treatment plans. This study focus on describing the changes in RT management based on QA round reviews in a single institution. MATERIALS AND METHODS: After 9 full years of implementation, a retrospective review of all patients whose charts passed through departmental QA chart rounds from 2007 to 2015. The reviewed cases were presented for RT plan review; subcategorized based on decision in QA rounds into: approved, minor modifications or major modifications. Major modification defined as any substantial change which required patient re-simulation or re-planning prior to commencement of RT. Minor modification included treatment plan changes which didn't necessarily require RT re-planning. RESULTS: Overall 7,149 RT treatment plans for different anatomical sites were reviewed at QA rounds. From these treatment plans, 6,654 (93%) were approved, 144 (2%) required minor modifications, while 351 (5%) required major modifications. Major modification included changes in: selected RT dose (96/351, 27%), target volume definition (127/351, 36%), organs-at-risk contouring (10/351, 3%), dose volume objectives/constraints criteria (90/351, 26%), and intent of treatment (28/351, 8%). The RT plans which required major modification according to the tumor subtype were as follows: head and neck (104/904, 12%), thoracic (12/199, 6%), gastrointestinal (33/687,5%), skin (5/106, 5%), genitourinary (16/359, 4%), breast (104/2387, 4%), central nervous system (36/846, 4%), sarcoma (11/277, 4%), pediatric (7/251, 3%), lymphoma (10/423, 2%), gynecological tumors (2/359, 1%), and others (11/351, 3%). CONCLUSION: Multi-disciplinary standardized QA chart rounds provide a comprehensive and an influential method on RT plans and/ or treatment decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".