Outcomes of Peer Review for Radiotherapy Treatment Plans With Palliative Intent
Bibliographic record
Abstract
PURPOSE: Peer review of a proposed treatment plan is increasingly recognized as an important quality activity in radiation medicine. Although peer review has been emphasized in the curative setting, applying peer review for treatment plans that have palliative intent is receiving increased attention. This study reports peer review outcomes for a regional cancer center that applied routine interprofessional peer review as a standard practice for palliative radiotherapy. METHODS AND MATERIALS: Peer review outcomes for palliative radiotherapy plans were recorded prospectively for patients who began radiotherapy between October 1, 2015, and September 30, 2017. Recommended and implemented changes were recorded. The content of detailed discussions was recorded to gain insight into the complexities of palliative treatment plans considered during peer review. RESULTS: Peer review outcomes were reviewed for 1,413 treatment plans with palliative intent. The proportions of detailed discussions and changes recommended were found to be 139 (9.8%) and 29 (2.1%), respectively. The content of detailed discussions and changes recommended was categorized. Major changes represented 75.9% of recommended changes, of which 84.2% were implemented clinically. CONCLUSION: Many complexities exist that are specific to palliative radiotherapy. Interprofessional peer review provides a forum for these complexities to be openly discussed and is an important activity to optimize the quality of care for patients with treatment plans that have palliative intent.
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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 | 0.058 | 0.450 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".