Can recurrence patterns after curative resection for gastric adenocarcinoma (GCa) inform the selection of adjuvant treatment?
Bibliographic record
Abstract
136 Background: Despite improvements in multidisciplinary care and surgical technique, ≈40% of patients recur after curative resection for GCa. Adjuvant treatment decisions should be informed by the expected timing and site of failure, but accurate data are limited. The aim of our study was to describe and analyze the patterns of recurrence at our center. Methods: Patients who underwent curative intent margin-negative resection from 2006-16 were identified from a prospective GCa database. Date of first detection and site of recurrence were determined. Survival curves were calculated by the KM method. Univariate and multivariable analyses (MVA) were performed to identify predictive variables. Significance was set at p < 0.05. Results: Of 123 patients who met inclusion criteria, median age was 70 yrs, 37% were female, and median follow-up after resection was 24 mos (IQR 11-40). Five-year OS was 72% and RFS 69%. In 34 of 36 patients who recurred, recurrence was detected within 2 yrs. Receipt of adjuvant therapy (Macdonald or MAGIC) was associated with better 5-year OS and RFS than surgery alone (79% vs. 60%, 77% vs. 60%) and delayed time to recurrence (median 10.4 vs. 7.1 mos). Receipt of CRT by the Macdonald protocol appeared to lower the risk of locoregional recurrence vs. surgery alone (see Table) despite routine D2 lymphadenectomy. On MVA, recurrence at any site was predicted by age £ 60y (OR 3.8), male gender (4.0), and AJCC stage > II (4.6). Peritoneal recurrence was predicted only by age £ 60y (3.2). Conclusions: Recurrence after curative resection occurred within 2 yrs of resection. Locoregional recurrence was uncommon in patients who received postoperative CRT. Peritoneal recurrence, which might be abrogated by prophylactic HIPEC at the time of resection, is not well explained by clinicopathologic variables, and novel molecular predictors are required. [Table: see text]
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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 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".