Outcomes by Treatment Modality in Elderly Patients with Localized Gastric and Esophageal Cancer
Bibliographic record
Abstract
Background: We aimed to assess current treatment patterns and outcomes in elderly patients with localized gastric and esophageal (GE) cancers. Methods: This retrospective analysis considered patients 75 years of age or older with ge cancers treated during 2012–2014. Patient demographics and tumour characteristics were collected. Overall survival (OS) and disease-free survival were assessed by univariable and multivariable Cox proportional hazards regression, adjusting for demographics. Logistic regression analyses were used to examine factors affecting treatment choices. Results: The 110 patients in the study cohort had a median age of 81 years (range: 75–99 years). Primary disease sites were esophageal (55%) and gastric (45%). Treatment received included radiation therapy alone (29%), surgery alone (26%), surgery plus perioperative therapy (14%), chemoradiation alone (10%), and supportive care alone (14%). In multivariable analyses, surgery (hazard ratio: 0.48; 95% confidence interval: 0.26 to 0.90; p = 0.02) was the only independent predictor for improved os. Patients with a good Eastern Cooperative Oncology Group performance status (p = 0.008), gastric disease site (p = 0.02), and adenocarcinoma histology (p = 0.01) were more likely to undergo surgery. Conclusions: At our institution, few patients 75 years of age and older received multimodality therapy for localized ge cancers. Outcomes were better for patients who underwent surgery than for those who did not. To ensure optimal treatment selection, comprehensive geriatric assessment should be considered for patients 75 years of age and older with localized GE cancers.
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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".