Impact of neoadjuvant therapy on short-term outcomes after left pancreatectomy: A propensity score–matched international multicenter study
Bibliographic record
Abstract
BACKGROUND: Neoadjuvant therapy is protective of postoperative pancreatic fistula in pancreatoduodenectomy. However, the effect of neoadjuvant therapy after left pancreatectomy remains unclear. The aim of this international multicenter study was to evaluate the impact of neoadjuvant therapy on short term outcomes after left pancreatectomy for pancreatic ductal adenocarcinoma. METHODS: Patients undergoing left pancreatectomy from January 2010 to April 2023 at 9 high-volume centers were included. Patients treated with neoadjuvant therapy were compared to patients with upfront surgery. Propensity score matching in 1:1 fashion was used. The primary outcome was postoperative pancreatic fistula. RESULTS: Six-hundred-fifty patients underwent resection due to pancreatic ductal adenocarcinoma, of which 70 patients (10.8%) received neoadjuvant therapy. In the matched cohort (upfront surgery, 66 patients; neoadjuvant therapy, 66 patients), the rate of postoperative pancreatic fistula was similar in patients undergoing upfront surgery versus patients receiving neoadjuvant therapy (16 [24.2%] vs 13 [19.7%], P = .674, respectively). No statistically significant differences were observed between neoadjuvant therapy and upfront surgery group with respect to grade C-POPF, readmission, reoperation, postpancreatectomy hemorrhage, 90-day mortality, and severe complications. CONCLUSION: Neoadjuvant therapy was not associated with decreased rate of postoperative pancreatic fistula in patients undergoing left pancreatectomy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".