Outcome of Multimodality Therapy for Elderly Colorectal Cancer Patients
Bibliographic record
Abstract
The aim of this study was to analyze patterns of multimodality therapy in elderly patients with advanced colorectal cancer. We enrolled 272 patients with colorectal cancer. All patients received chemotherapy and some patients received secondary cytoreductive surgery and/or radiofrequency ablation. We compared differences between elderly patients (age >=75 years) and non-elderly patients (age <75 years), especially in relation to multimodality therapy. There were no significant differences in cancer-specific survival between elderly (n = 37) and non-elderly patients (n = 235).Twenty-seven percent of elderly and 35% of non-elderly patients received multimodality therapy, which resulted in prolonged survival. Although the main chemotherapy regimen was the same in both groups who received multimodality therapy, elderly patients who received chemotherapy alone seemed to be under-treated. For elderly patients, prognostic factors were host-related, such as comorbidities, whereas for non-elderly patients prognostic factors were tumor-related. Comorbidities and modified Glasgow Prognostic Score may be prognostic indicators in elderly patients receiving multimodality therapy. In conclusion, chronological age alone should not contraindicate multimodality therapy of colorectal cancer in elderly patients. Appropriate selection criteria for multimodality therapy in elderly patients should include not only tumor characteristics, but also host- and treatment-related factors.
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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 | 0.000 | 0.000 |
| 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".