Population-based patterns of palliative systemic therapy use in elderly patients with metastatic colorectal cancer (mCRC).
Bibliographic record
Abstract
9547 Background: Elderly cancer patients are consistently under-represented in clinical trials, which may lead to under-treatment. Our aims were to 1) determine the impact of advanced age on use of palliative systemic therapy in mCRC, 2) examine the reasons for treatment choices and 3) compare adverse events and treatment discontinuations in elderly vs young patients. Methods: All patients diagnosed with mCRC from 2006 to 2007 and referred to any 1 of 5 regional cancer centers in British Columbia, Canada were reviewed. Summary statistics were used to describe treatment patterns between elderly patients (EP; >/=70 years) and young patients (YP; <70 years). Cox regression models that adjusted for age and confounders were used to determine the effect of systemic therapy on overall survival (OS). Results: We identified 1,013 patients: median age was 67 years (range 23-93); 42% were elderly and 58% were young; 57% were men; and 66% had ECOG 0/1. Compared to YP, fewer EP were offered systemic therapy (46 vs 76%, p<0.001). Among those treated, EP were less likely than YP to be given combination chemotherapy (47 vs 81%, p<0.001) and bevacizumab (19 vs 47%, p<0.001). Most common reasons for no treatment were similar in EP and YP: patient choice (32% for both), poor ECOG (18% of EP and 16% of YP), and significant comorbidities (11% for both). Advanced age alone was also cited as a reason among EP (7%) for not receiving therapy. In the subset that was treated, risk of adverse events (24 vs 14%, p=0.24) and early treatment discontinuations (14 vs 13%, p=0.88) were comparable between EP and YP, respectively. Receipt of systemic therapy was associated with improved OS in both the elderly (HR for death 0.45, 95% CI 0.37-0.56, p<0.001) and the young (HR for death 0.43, 95% CI 0.35-0.53, p<0.001), regardless of age (p interaction of age and treatment >0.05). Conclusions: In this population-based cohort of mCRC, EP were more likely to receive no treatment, monotherapy rather than combination therapy, or a regimen without bevacizumab. In carefully selected EP, however, it appears that rate of adverse events, frequency of early treatment discontinuations, and magnitude of survival benefit from systemic therapy were comparable to YP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".