Current Treatment Approaches and Outcomes in the Management of Rectal Cancer Above the Age of 80
Bibliographic record
Abstract
Background: The number of cases of rectal cancer in our older cohort is expected to rise with our ageing population. In this study, we analysed patterns in treatment and the long-term outcomes of patients older than 80 years with rectal cancer across a health district. Methods: All cases of rectal cancer managed at the Illawarra Cancer Care Centre, Australia between 2006 and 2018 were analysed from a prospectively maintained database. Patients were stratified into three age groups: ≤65 years, 66–79 years and ≥80 years of age. The clinicopathological characteristics, operative and non-operative treatment approach and survival outcomes of the three groups were compared. Results: Six hundred and ninety-nine patients with rectal cancer were managed, of which 118 (17%) were aged 80 and above. Patients above 80 were less likely to undergo surgery (71% vs. 90%, p < 0.001) or receive adjuvant/neoadjuvant chemoradiotherapy (p < 0.05). Of those that underwent surgical resection, their tumours were on average larger (36.5 vs. 31.5 mm, p = 0.019) and 18 mm closer the anal verge (p = 0.001). On Kaplan–Meier analysis, those above 80 had poorer cancer-specific survival when compared to their younger counterparts (p = 0.032), but this difference was no longer apparent after the first year (p = 0.381). Conclusion: Patients above the age of 80 with rectal cancer exhibit poorer cancer-specific survival, which is accounted for in the first year after diagnosis. Priority should be made to optimise care during this period. There is a need for further research to establish the role of chemoradiotherapy in this population, which appears to be underutilised.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".