Toward quality peer review: Outcomes of peer review across provincial radiation oncology programs.
Bibliographic record
Abstract
205 Background: Review of treatment plans by a second radiation oncologist is an important quality indicator in radiation oncology. Peer review (PR) can improve quality of care in individual patients by detecting clinical and planning issues and recommending plan changes. This study reports the frequency and nature of these changes across all 14 radiation oncology programs (ROPs) in Ontario, Canada. Methods: We identified all peer-reviewed curative treatment plans delivered in Ontario within a 3-month study period between Dec 2013-Nov 2014 using Cancer Care Ontario’s Activity Level Reporting System, where data on treatment intent and date, disease site treated, PR status, timing of PR, and nature of recommended changes were available. Results: There was considerable variation in the proportion of plans peer-reviewed across ROPs (70.2%, range: 40.8-99.2%). Over the study period, 5,561 curative treatment plans were peer-reviewed and 3.3% had changes recommended. Of those, 21.0% had major clinical and re-planning implications. Recommended changes most often involved minor (63.1%) vs major (36.9%) re-planning implications. Highest proportions of changes were recommended for the treatment of the esophagus, uterus, upper limb, cervix, lower limb, H&N, bilateral lung, right supraclavicular nodes, rectum, and spine (5.0%-7.0%). Plans involving the left breast had slightly more changes recommended (3.0% [95%CI:2.0%-4.5%]) vs right breast (2.4% [95%CI:1.5%-3.8%]). Recommendations were more frequently made when PR was conducted pre-radiotherapy (3.8%) vs during (1.4%-2.8%; p = 0.005), however the nature and implementation of changes were not statistically associated with the timing of PR (p = 0.91; p = 0.23, respectively). Proportion of recommended changes to treatment plans was not statistically associated with ROP patient volume (p = 0.08), proportion of plans peer-reviewed (p = 0.36) or academic status (p = 0.75). Conclusions: Significant variation exists in the proportion of recommended changes across all disease sites and ROPs. PR seems effective in detecting treatment plans with important clinical and planning issues; strategies should be developed to optimize its conduct in radiation oncology.
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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 | 0.136 | 0.519 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".