Adjuvant treatment of colorectal cancer in the elderly: Where do we come from and where are we going?
Bibliographic record
Abstract
Objective: Colorectal cancer is the third most commonly reported cancer in the world and about 50% of patients are diagnosed over the age of 70 years. The authors discuss age-related changes in organ function, comorbidities and frailty in the elderly, and their impact on chemotherapy toxicity. Methods: The authors review data from observational studies and subgroup analyses of randomized clinical trials on adjuvant chemotherapy in elderly colorectal cancer patients. Results: Several large population-based studies suggest that adjuvant chemotherapy is offered less frequently to elderly patients, although in recent years the prescription patterns tended to significantly increase. In fact, data from retrospective analyses of randomized trials indicate that elderly stage III colorectal cancer patients may get similar clinical advantage from adjuvant treatment with fluoropyrimidines, although major comorbidities may substantially limit life expectancy and minimize the survival benefits. The use of oxaliplatin-based regimens need s to take into account the individual risk/benefit profile due to lack of unequivocal evidence of positive literature data. Conclusions: Adjuvant chemotherapy of colorectal cancer should be investigated by prospective trials specifically designed for the elderly. Fit elderly patients should be offered standard adjuvant treatments, while modified schedule, attenuated doses or even treatment omission can be offered to more frail 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".