Reporting on quality indicators in renal cell carcinoma: Proportion of metastatic patients who initially commence on full-dose targeted therapy.
Bibliographic record
Abstract
265 Background: Measuring quality of care is required to improve patient outcomes. There are 23 established Quality Indicators in Renal Cell Carcinoma (RCC) (Wood LA et al, JOP 2013) including initiating targeted therapy (TT) at full dose. It is recognized that the dose of TT is important to optimize clinical outcomes. Methods: The Canadian Kidney Cancer Information System (CKCis) is a prospectively maintained database of RCC pts from 14 Canadian academic centers. CKCis data was used to determine the use of TT in metastatic pts, the initial dose in the first line setting, and factors influencing whether pts initiated full dose TT. Descriptive statistics were performed to characterize current practices and a multivariate logistic regression was performed to assess predictors of initiating full dose TT. Results: Between 2006 and 2014, 560 pts received first-line TT and make up the study cohort. 92% received VEGF TT (81% sunitinib; 9% pazopanib; 2% sorafenib) and 8% received mTOR TT (5% everolimus; 3% temsirolimus). All pts receiving mTOR inhibitors received full dose and are excluded from further analysis. Full dose VEGF TT was initiated in 87% on sunitinib, 78% on pazopanib, and 92% on sorafenib. The sunitinib schedule was 4/2 in 81%, 2/1 in 12%, and other in 7%. Pts were less likely to initiate full dose TT if they were older (OR: 1.10, 95% CI 1.06-1.15), female (OR: 2.23, 95% CI 1.09-4.55) or had a worse ECOG PS (2,3,4 vs 0,1) (OR: 2.30, 95% CI 0.95-5.59). Data on initial versus final dose of TT will also be presented. Conclusions: Maximizing exposure to TT is important in metastatic RCC which is why initiating full dose TT was prioritized as a key QI in RCC. In this study, 87% of pts were initiated on full dose VEGF TT in the first line setting, and 100% of pts on mTOR TT which is higher than predicted. Those less likely to start on full dose VEGF TT were older, female and had a lower ECOG PS. The gender discrepancy will be explored in future studies.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".