Outcomes Following Colorectal Cancer Resection in Elderly Patients
Bibliographic record
Abstract
Background: Colorectal cancer (CRC) mainly affects older adults, yet elderly patients are underrepresented in outcomes research. Accurate risk stratification tools, such as the Charlson Comorbidity Index (CCI), are essential for guiding surgical decisions in this group. Methods: We conducted a retrospective review of patients aged 75 years or older who underwent colorectal cancer resection at a tertiary centre between January 2019 and September 2024. Clinical, pathological, and molecular data were analyzed. The primary outcome was a composite of major postoperative complications (Clavien–Dindo grade 3 or higher) or 30-day mortality, stratified by CCI (5 or higher vs. less than 5). Statistical tests included chi-square, Fisher’s exact, and Mann–Whitney U as appropriate. Results: The median age was 81 years (range 75–97), with 59.7% male. CCI ≥ 5 was observed in 24.6% (51/211). The primary composite outcome of major postoperative complications or 30-day mortality occurred in 15/51 (29.4%) patients with a CCI ≥ 5 compared to 19/160 (11.9%) with a CCI < 5 (p = 0.04). Major complications occurred in 18.5% (39/211) of cases, and the 30-day mortality rate was 3.3% (7/211). Laparoscopic resection was independently protective in multivariate analysis (adjusted OR 0.37, p = 0.048), while age ≥85 and emergency presentation were not statistically significant predictors. Conclusions: Colorectal resection in patients aged ≥75 is linked with acceptable morbidity and low short-term death rates. A CCI ≥ 5 significantly predicts adverse outcomes and should be included in preoperative assessments. Minimally invasive surgery seems advantageous and should be considered, when possible, to enhance results in this high-risk group.
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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 | 0.003 |
| 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".