Assessing guideline impact on referral patterns of postprostatectomy patients to radiation oncologists
Bibliographic record
Abstract
INTRODUCTION: Adjuvant radiotherapy (aRT) can improve biochemical progression-free survival in patients with high-risk features (HRF) after radical prostatectomy (RP). Guidelines from Alberta and the Genitourinary Radiation Oncologists of Canada (GUROC) recommend that patients with HRF be referred to radiation oncologists (RO) based on the findings from three randomized, controlled trials (RCT). Our study examines the impact of these recommendations both pre- (2005) and post- (2012) publication of RCT and GUROC guideline establishment. METHODS: Patients undergoing RP during 2005 and 2012 were identified from the provincial cancer registry. Charts were retrospectively reviewed and variables of interest were linked to the registry data. RO referral patterns for each year were determined and variables influencing referral (extracapsular extension, positive margin, seminal vesicle invasion, and post-RP prostate-specific antigen [PSA]) were compared. RESULTS: Median time to referral was 26.4 months in 2005 compared to 3.7 months 2012 (p<0.001). Among patients referred post-RP, a higher proportion was referred within six months in 2012 (21%) as compared to 2005 (13%) (p=0.003). Among eligible patients in 2012, 30% were referred for discussion of aRT compared to 24% in 2005 (p=0.003). There was a marked drop in patients referred for salvage radiation therapy beyond six months and a rise in the number of patients who are never referred. CONCLUSIONS: Despite an increase in referral rates to RO post-RP from 2005-2012, more than 50% of those patients with HRF did not receive a referral. Initiatives aimed at improving multidisciplinary care and guideline adherence should be undertaken.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".