855 OPTIMIZATION OF SURGICAL DELAY AFTER INDUCTION CHEMORADIATION FOR ESOPHAGEAL CANCER: 10 YEARS RETROSPECTIVE MULTI-CENTER STUDY
Bibliographic record
Abstract
Abstract The treatment of esophageal cancer is in constant evolution. Most of the esophageal cancer receive induction chemoradiation therapy. Surgical delay has been studied but the optimal timing has not been clarified. Through the years, surgical delay has been modified by surgeons in our institutions, going from an average of 6 weeks delay to an average of 10 weeks delay. It is time to ask if this change has a real positive impact on our patient. Methods In this retrospective multi-center study, we combined data from two center in Quebec city that performs oncologic esophagectomy. The surgical delay went from 6 to 10 weeks around 2014. All surgeons changed their practice at that moment. We retrospectively analysed 5 years before and after the change of practice and created two cohorts of patients. Our primary outcome compared complete pathologic response rate. Our secondary outcomes were surgical complications, anastomotic leak, disease free survival and overall survival. Results Thirty-eight patients had surgery under 8 weeks (mean: 6 weeks) after their induction chemoradiation compared to 64 patients that had surgery after 8 weeks (mean: 10 weeks). There was no statistical significant difference between groups for the complete pathologic response (32% vs 25%, p = 0,16). Important complications were similar, with a rate of 24% vs 28% (p = 0,69). Anastomotic leaks were less frequent in the less than 8 weeks group, but no statistical significance was obtained (13% vs 27%, p = 0,14).No difference in the disease-free survival rate and overall survival rate was noted (DFS 40% vs 55% (p = 0,32), OS 38% vs 38% (p = 0,29)). Conclusion The treatment of esophageal cancer is in constant evolution, induction therapy and surgical technics involve over time. Surgical delay has no impact on complete pathologic response, complication and overall survival. There is no advantage to wait longer before surgery.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".