Quality of radiation therapy referral and utilization post-prostatectomy: A population-based study of time trends.
Bibliographic record
Abstract
286 Background: Adjuvant radiotherapy (ART) post-radical prostatectomy (RP) has been shown to benefit patients with pathologic T3 or margin-positive prostate cancer. It remains unclear whether early salvage radiotherapy (SRT) confers equivalent outcomes to ART. Practice Guidelines recommend referral to radiation oncology (RO) within 6 months of RP to discuss ART and SRT. The study objectives were to (1) assess post-RP referral patterns to RO; (2) describe ART and SRT utilization; and (3) compare time trends before and after seminal trials and guidelines were published. These provide indications of access to quality care. Methods: This was a retrospective cohort study. Electronic clinic visit and RT treatment records were linked to the population-based Ontario Cancer Registry. The study population included all prostate cancer cases treated with RP in Ontario January 1, 2003 - November 30, 2012. ART was defined as curative RT within 6 months of RP, and SRT was 6 - 24 months post-RP. Changes in RO referral and RT rates over time were statistically analyzed using the Cochran-Mantel-Haenszel Chi-Square test. Results: Over the study period, 30,447 prostate cancer patients received RP and 15.2% saw an RO within 6 months of RP. This proportion doubled between 2003 and 2012 (from 10.7% in 2003-2004 to 21.7% in 2011-2012, p < 0.001 for trend). The annual percentage change was largest 2009-2011 (3.4%). In comparison, the proportion seen within 24 months of RP remained stable at 32.3% ± 1.4%. Amongst the 4,641 patients seen by an RO within 6 months of RP for consideration of ART or SRT, the proportion receiving ART remained relatively constant at 51.0% ± 3.0%. Commensurate with RO referral trends, there was a doubling in ART rates amongst all RP cases, ranging from 5.4% in 2003-2004 to 11.0% in 2011-2012 (p < 0.001), compared to relatively stable SRT rates of 8.5% ± 0.2% (7.9% in 2003-2004, 8.9% in 2010-2011). Consequently, the total proportion receiving RT within 24 months of RP increased from 14.1% in 2003-2004 to 19.8% in 2010-2011 (p < 0.0001). Conclusions: There was an increase in access to early RO referral post-RP and in ART utilization in Ontario from 2003 to 2012, following publication of key clinical trials and guidelines.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".