767. INFLUENCE OF NEOADJUVANT IMMUNOTHERAPY-CHEMOTHERAPY ON PERIOPERATIVE OUTCOMES IN LOCALLY ADVANCED ESOPHAGEAL ADENOCARCINOMA
Bibliographic record
Abstract
Abstract Background This study evaluates the perioperative outcomes of patients with locally advanced esophageal adenocarcinoma (EAC) who were treated with neoadjuvant immunotherapy (IO) and chemotherapy versus a matched cohort of patients that received neoadjuvant chemotherapy (NAC) alone. Methods A single center non-randomized phase II trial was undertaken in locally advanced (cT3-4 and/or N+) EAC and 49 patients completed neoadjuvant Avelumab + DCF (Docetaxel, Cisplatin, 5FU) and esophagectomy between 2/2018 and 2/2020. These patients were matched to contemporary patients (1/2018-6/2020) who met inclusion criteria but received neoadjuvant chemotherapy alone (NAC) with a comparable Docetaxel based. The postoperative outcomes were then compared between the two groups. Results Ninety-nine patients with locally advanced EAC underwent esophagectomy and meet enrolment criteria for this study. Of these patients, 50 received NAC alone 49 received IO+NAC. Baseline characteristics such as age, gender and clinical stage were comparable between groups. Operative approach and rate of MIE (~60%) was similar in both groups. Overall and major complication rate were similar between groups (50 vs. 51%, p=0.91; 20 vs. 26%, p=0.44 respectively) with concordant rates of anastomotic leak (6 [12%] vs. 6 [12%], p=0.86) and respiratory complications (13 [26%] vs. 11 [22%], p=0.68) in NAC alone and IO+NAC group respectively. There were no significant differences in the LOS and 30- and 90-days mortality rates between the two groups. Conclusion The addition of immunotherapy to neoadjuvant chemotherapy for locally advanced esophageal adenocarcinoma does not appear to significantly alter perioperative short-term outcomes after esophagectomy
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".