7: NEOADJUVANT CHEMORADIOTHERAPY OR CHEMOTHERAPY ALONE FOR OESOPHAGEAL CANCER: POPULATION-BASED COHORT STUDY
Bibliographic record
Abstract
Abstract Background and aim Although both neoadjuvant chemoradiotherapy (nCRT) and chemotherapy (nCT) are used as neoadjuvant treatment for oesophageal cancer, it is unknown if one provides a survival advantage over the other particularly with respect to histological subtype. This study aimed to compare prognosis following nCRT and nCT in patients undergoing oesophagectomy for oesophageal adenocarcinoma (OAC) and squamous cell carcinoma (OSCC). Methods Data from the National Cancer Database (2006 to 2015) were used to identify patients with OAC (n = 11,167; nCRT 9,972 (89%), nCT 1,195 (11%)) and OSCC (n = 2,367; nCRT 2 m155 (91%), nCT 212 (9%)). Propensity score matching and Cox multivariable analyses were used to account for treatment selection biases. Results In the matched cohort for OAC, nCRT provided higher rates of complete pathological response (35% vs. 21%, P < 0.001) and margin-negative resections (90% vs. 86%, P < 0.001). However, nCRT had similar survival to nCT (Hazard ratio (HR):1.04, 95% confidence interval (CI):0.95–1.14). The corresponding 5-year survival for nCRT and nCT were 36% and 37% (P = 0.1), respectively. For OSCC, nCRT had higher rates of complete pathological response (51% vs. 30%, P < 0.001) and margin-negative resections (93% vs. 82%, P < 0.001). nCRT had a statistically significant overall survival benefit (HR: 0.78, 95% CI: 0.62–0.97). The 5-year survival for nCRT and nCT were 45% versus 38% (P = 0.026), respectively. Conclusion Despite pathological benefits including primary tumour response from nCRT, there was no prognostic benefit to nCRT compared to nCT for OAC suggesting both modalities are equally acceptable. However, for OSCC, nCRT followed by surgery appears to remain the optimal treatment approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".