A real-world comparison of multi-modality therapies in locally advanced gastro-esophageal junction (GEJ) cancers.
Bibliographic record
Abstract
96 Background: Trials show that addition of systemic therapy and/or radiation to surgery improves survival in GEJ cancers. However, the different regimens have not been directly compared. We examined population-based outcomes of 3 treatments: 1) neoadjuvant carboplatin and paclitaxel plus radiation (CROSS); 2) perioperative epirubicin, cisplatin, and fluoropyrimidine (MAGIC); and 3) cisplatin and fluoropyrimidine with radiation (CisFP). Methods: We reviewed patients diagnosed with GEJ cancer from 2005 to 2015 who received CROSS, MAGIC, or CisFP at 2 tertiary, 4 regional, and 11 community cancer centers in Alberta, Canada. Survival was assessed with Kaplan-Meier curves and compared with the log-rank test. A Cox proportional hazards model was constructed to evaluate the impact of treatment on overall survival (OS). Results: 331 patients were identified. Median age was 63 (IQR 56-69) years and 86% were men. CROSS was used in 217 (65%) cases followed by CisFP in 72 (22%) and MAGIC in 42 (13%). Age, sex, and stage were not associated with treatment selection (all p > 0.05), but a higher proportion of CROSS and CisFP patients had adenocarcinoma (86% and 85%, respectively) compared to MAGIC patients (41%) ( p < 0.01). CROSS and MAGIC correlated with higher surgical resection rates when compared to CisFP (82% vs. 79% vs. 50%, respectively, p < 0.01). Median OS favored CROSS and MAGIC rather than CisFP, but this was not statistically significant (29 vs. 34 vs. 20 months, respectively, p= 0.17). Adjusting for confounders, OS remained similar for MAGIC (HR 0.8, 95%CI 0.5-1.3, p= 0.36) and CisFP (HR 0.7, 95%CI 0.5-1.1, p= 0.10) when compared to CROSS. In addition, age > 65, advanced stage, and lack of surgical resection were associated with increased risk of death (HR 1.5, 95%CI 1.1-2.0, p= 0.02, HR 2.2, 95%CI 1.2-3.9, p< 0.01 and HR 4.1, 95%CI 2.8-5.9, p< 0.01, respectively). Conclusions: OS was similar across all 3 regimens, but outcomes were inferior to those seen in original trials. This observation suggests that GEJ patients in routine practice are different from study participants or that treatment selection may be driven by factors other than trial eligibility criteria.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".