Outcomes for patients ≥75 years with localized gastroesophageal cancer: Experience from the Princess Margaret Cancer Centre.
Bibliographic record
Abstract
189 Background: The optimal treatment and outcome for elderly patients (pts) with localized gastroesophageal (GE) cancer remains unclear as they are underrepresented in clinical trials. We aimed to assess survival in pts ≥ 75 years according to treatment received. Methods: A retrospective analysis was performed for all pts aged ≥ 75 years with GE cancer treated in 2012 and 2013. Frailty was measured using the Charlson comorbidity index (CCI) and ECOG performance status (PS). Overall survival (OS) and disease-free survival (DFS) were assessed via uni- and multivariable Cox proportional hazards regression, adjusting for demographics. Logistic regression analyses were used to examine factors impacting treatment choices. Results: Of 70 pts, median age was 82 years (range: 75-98), primary sites were esophageal (40%, with 61% squamous histology), GE junction (24%) and gastric (36%). Baseline characteristics included: PS: 0 (40%), 1 (39%), 2 (14%), 3 (7%); and CCI: 0 (36%), 1 (20%), 2 (21%), ≥ 3 (23%). Treatment received included surgery (33%), radiotherapy (RT) (31%); surgery plus adjuvant chemotherapy (chemo) and/or RT (9%); chemoradiation alone (7%) and 20% had no active treatment. In univariable analysis; age < 85 (p = 0.007) and surgery (p = 0.022) were associated with improved OS. Chemo and RT, either alone or in combination, did not significantly improve OS. In multivariable analysis; age < 85 (HR 0.46, 95% CI: 0.23-0.94, p = 0.034), surgery (HR 0.32, 95% CI: 0.14-0.74, p = 0.008) and CCI < 2 (HR 0.52, 95% CI: 0.27-0.99, p = 0.048) were identified as independent predictors for improved OS. Age ≥ 85 was significantly associated with omission of surgery (OR 3.61, 95% CI: 1.13-14.01, p = 0.041) but in contrast, PS ≥ 2 (p = 0.475) and CCI ≥ 2 (p = 0.939) were not predictive. Conclusions: At our institution, very few pts ≥ 75 years received multimodality therapy for localized GE cancers. Surgery was the only treatment modality associated with a significant survival advantage, and additional chemo and/or RT did not further improve OS. The only predictor for having surgery was age. Consequently, future studies should consider comprehensive assessment for surgery so that eligible elderly pts can benefit.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".