Use of palliative chemotherapy and targeted agents in elderly patients with metastatic colorectal cancer (mCRC).
Bibliographic record
Abstract
6018 Background: Elderly patients are increasingly diagnosed with advanced cancers, but they are consistently underrepresented in clinical trials, which may lead to undertreatment. Our aims were to 1) evaluate the impact of advanced age on patterns of first-line chemotherapy and bevacizumab use in mCRC, 2) examine the reasons for treatment choices and 3) compare adverse events and treatment discontinuations in elderly vs young patients. Methods: A random sample of mCRC patients diagnosed from 2006 to 2007 and referred to any 1 of 5 regional cancer centers in British Columbia, Canada was reviewed. Summary statistics were used to describe treatment patterns between the elderly (>/=70 years) and young (<70 years). Cox regression was used to determine the effect of systemic therapy on overall survival, controlling for age and confounders. Results: We identified 800 patients: 43% elderly and 57% young; 56% men; and 26 / 36 / 38% ECOG 0 / 1 / 2+, respectively. Fewer elderly patients were given chemotherapy (52% vs 79%, p<0.001). Among those treated, most common first-line palliative regimens for elderly vs young included: capecitabine (50 vs 15%), FOLFIRI (26 vs 38%), and FOLFOX (15 vs 37%) (all p<0.001). Those aged >/=70 were also less likely to receive bevacizumab in their regimens (22 vs 50%, p<0.001). The most frequent reasons for no systemic therapy were similar between age groups: patient choice (31 vs 28%), poor ECOG (16 vs 17%), and significant co-morbidity (11 vs 13%). Risk of chemotherapy (p=0.30) and bevacizumab (p=0.39) adverse events were comparable between elderly and young as were rates of early chemotherapy (p=0.07) and bevacizumab (p=0.79) discontinuation. Receipt of systemic therapy +/- bevacizumab was associated with improved survival from mCRC (HR for death 0.50, 95% CI 0.31-0.62, p<0.001), regardless of advanced age (p interaction for age and treatment = 0.33). Conclusions: Elderly patients with mCRC are more likely to receive no chemotherapy, capecitabine monotherapy, or a regimen without bevacizumab. However, in carefully selected elderly patients, adverse events, treatment discontinuations, and overall survival benefit from treatment appear similar to those observed for younger patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".