Characteristics and treatment effect of senior patients with metastatic colorectal cancer (mCRC): A retrospective analysis.
Bibliographic record
Abstract
600 Background: Colorectal cancer affects many elderly patients as approximately 50% of cases are diagnosed in patients older than 70 years of age. We retrospectively studied characteristics, presentation, treatment and clinical course of our senior patients with (mCRC). Methods: Medical records of all patients ≥70 years with mCRC treated in our institution from 2006-2009 were reviewed. A multivariate Cox's proportional hazards model was applied to estimate the effect of chemotherapy (CT) on survival, while adjusting for other factors measured at time of diagnosis (age, sex, comorbidity, and number of metastases). Results: Ninety-four patients (48 men and 46 women) ≥70 year-old with mCRC were identified. Abdominal pain, fever, lower GI bleed and anemia were the most common presenting symptoms. Fifty-seven patients didn't receive CT mainly because of patient/family refusal, alteration of general status and presence of comorbidity. CT (N=37) was associated with an improved median survival (13.5 months) compared to non-treated group (2 months), with hazard ratio (0.20; 95% CI, 0.09 to 0.43; p<0.00001). In the multivariate analysis, age (70-74 vs 75-79 vs ≥80 years), sex, comorbidity (0 vs 1 vs 2 vs ≥3), and number of metastatic sites (1 vs ≥2) were not independently associated with survival. ECOG-PS was not mentioned in the medical record of most patients. Sixty- three percent of the 70-74 year-old patients, 34% of the 75-79 year-old group, and only 23% of the ≥80 year-old patients received CT. Treatment benefit was seen with either 5-FU monotherapy (HR: 0.30; 95% CI, 0.12 to 0.77), Irinotecan-based (HR: 0.24; 95% CI, 0.07 to 0.74), or Oxaliplatin-based CT (HR; 0.07; 95% CI, 0.01 to 0.30). None of the patients had geriatric assessment prior to treatment. Conclusions: Most elderly patients with mCRC do not receive CT, yet treatment was associated with a clear survival benefit. The presence of multiple comorbidities did not affect treatment benefit. Although performance status could not be ascertained in this retrospective study, it likely influenced survival and the decision to treat with CT or not. Oncogeriatric assessment may be useful in better selecting elderly patients for systemic treatment. No significant financial relationships to disclose.
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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 | 0.001 |
| 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".