Comparison of patient characteristics and chromosomal abnormalities by first-line treatment in chronic lymphoid leukemia (CLL) patients in Canada.
Bibliographic record
Abstract
e18029 Background: With recent approval of targeted agents and continued development of novel therapies, the first-line treatment of patients with chronic lymphocytic leukemia (CLL) has undergone a significant evolution in the past year. This retrospective study investigates patient characteristics and genetic abnormalities by first-line treatment choice in CLL patients in Canada. Methods: This study utilized IMS Brogan Enhanced Tumor Studies, an anonymised patient database collected through quarterly physician panel survey, which provides comprehensive insight into total cancer care. Patient characteristics (including age, co-morbidities and ECOG performance status), genetic markers and first-line drug treatments were identified in CLL patients treated in Canada between October 2013 and September 2014. Results: See Table. Analysis of 273 first-line CLL patients identified that younger patients (≤70) with more favorable disease characteristics (co-morbidities=0-1, good renal function, & ECOG=0-1) were predominantly treated with fludarabine-based regimens in the first-line. Older patients (>70) with less favorable disease characteristics (co-morbidities=2+, renal disease & ECOG=2-3) were mostly treated with chlorambucil-based regimens, bendamustine-based regimens and novel treatments in the first-line (see table). Of the 190 first-line CLL patients (70%) that were tested for chromosome abnormalities, 63% tested positive. 53% of patients possessing deletion 13q or trisomy 12 abnormalities were prescribed fludarabine-based regimens. Conversely, 44% of patients with 17p or 11q deletions were prescribed chlorambucil-based regimens. Conclusions: The analysis of first-line CLL patients in this study has identified differences in patient fitness and genetic markers. It therefore demonstrates the importance of these factors in determining treatment choice. First-line Treatment FLUD-based (exc. FCR) (n=75) FCR (n=45) CHLOR-based (n=78) BEND-based (n=21) NOVEL (n=5) Age ≤70: 68% ≤70: 86% >70: 59% >70: 62% >70: 25% Co-morbidities 0-1: 87% 0-1: 82% 2+: 41% 2+: 48% 2+: 40% Renal Disease 4% 9% 12% 29% 20% ECOG Status 0-1: 63% 0-1: 72% 2-3: 36% 2-3: 52% 2-3: 60%
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".