Examining Treatment Patterns and Real-World Outcomes in Chronic Lymphocytic Leukemia Using Administrative Data in Ontario
Bibliographic record
Abstract
Information on the real-world experience of Canadians diagnosed with chronic lymphocytic leukemia (CLL) is limited. This study was conducted to report treatment patterns and outcomes of CLL using Ontario administrative data. A retrospective cohort study was conducted in patients diagnosed with CLL between 1 January 2010 and 31 December 2017 identified in the Ontario Cancer Registry (OCR). Data were accessed using the Institute of Clinical Evaluative Sciences (ICES), which collects various population-level health information. In the Ontario Cancer Registry, 2887 CLL patients receiving treatment and diagnosed between 2010-2017 were identified. Fludarabine, cyclophosphamide and rituximab (FCR) chemoimmunotherapy was most frequently used as a first line, but use declined since ibrutinib and obinutuzumab combinations were funded in 2015. In patients treated with frontline FCR, survival at year one was 89% pre-2015 and 96% post-2015; at year four, survival was 73% and 87%, respectively. Survival in patients treated with frontline chlorambucil was 76% pre-2015 and 75% post-2015 in year 1, and 45% and 56% in year 3. Our analysis shows that, as the treatment landscape for CLL has shifted, use of newer and novel agents as a first line or earlier in the relapsed/refractory setting has resulted in improved survival outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".