A prospective Canadian gastroesophageal cancer database: What have we learned?
Bibliographic record
Abstract
Background: Minimal literature exists on outcomes for Canadian patients with gastroesophageal adenocarcinoma (GEA). The objective of our study was to establish a prospective clinical database to evaluate demographic characteristics, presentation and outcomes of patients with GEA. Methods: Patients diagnosed with GEA were recruited from Jan. 30, 2017, to Aug. 30, 2020. Data collected included demographic characteristics, presentation, treatment and survival. A multivariable model for overall survival in patients treated with curative intent was created using sex, lymph node status, resection margin status, age and tumour location as variables. Results: A total of 122 patients with adenocarcinoma of the stomach or gastroesophageal junction were included. Median age was 65 years (interquartile range [IQR] 59–74), 70% of patients were male and 26% were born outside of Canada. Median follow-up time was 14.5 (IQR 8.0–31.0) months. Following staging computed tomography scanning, 88% of patients were deemed to have potentially resectable disease. Eighty-one (76%) received staging laparoscopy and 74 (61%) were treated with curativeintent surgery. Forty-six (62%) patients had nodal metastases. The median number of nodes harvested was 22 (IQR 18–30). The R0 resection margin rate was 82%. The 3-year overall survival for patients who received curative-intent treatment was 63% and 38% for all patients. On multivariable analysis, female sex (hazard ratio [HR] 3.88, p = 0.01), positive nodal status (HR 3.58, p = 0.02), positive margins (HR 3.11, p = 0.03) and tumour location (HR 3.00, p = 0.03) were associated with decreased overall survival. Conclusion: Many of the patients with GEA in this study presented with advanced disease, and only 61% were offered curative-intent surgery. A prospective multicentre national GEA database is now being established.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".