Trends in survival based on treatment modality for esophageal cancer: a population-based study
Bibliographic record
Abstract
OBJECTIVES: The primary objective was to examine the trends in treatment modalities and the respective survival rates for esophageal cancer in the province of Ontario, Canada. METHODS: This is a population-based study of all esophageal cancer cases diagnosed in Ontario between 2007 and 2015, including squamous cell carcinoma and adenocarcinoma, with known disease stage. Other characteristics include sex, age, date of diagnosis, and treatment modalities. Treatment modalities were classified as no-treatment, radiation only or chemotherapy only, chemoradiation, and surgical resection. RESULTS: In total, 2572 patients were identified with esophageal cancer from 2007 to 2015, of which 2014 (78.3%) were male. The mean age at diagnosis was 66.6 (SD = 11.7) years. Survival rate increased over time in patients who underwent chemoradiation or surgical resection but remained unchanged for the radiation-only or chemotherapy-only group and decreased for the no-treatment group. Survival considerably improved (15-20%) for patients with stages I-III disease. CONCLUSIONS: The positive trends in the survival rate for esophageal patients could be due to adoption of multimodal therapy. Despite a lower proportion of advanced disease among patients over 80, they received less curative treatments compared with other age groups. Further studies are required to identify strategies to maximize survival for patients with stage IV disease, and patients 80 years and older.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".