Impact of Diabetes on Short-Term and Long-Term Outcomes of Ampullary Adenocarcinoma Patients after Curative Pancreatoduodenectomy
Bibliographic record
Abstract
Background: Many studies have confirmed that diabetes was associated with prognosis in many malignant cancer types. However, the impact of diabetes on ampullary carcinoma (AC) has not been investigated. Methods: A total of 266 AC patients in the National Cancer Center of China between January 1998 and December 2020 were retrospectively reviewed. The postoperative complication rate, postoperative recurrence rate, and long-term survival were compared between the diabetes group and the no diabetes group. Results: A total of 32 AC patients (12.03%) were diagnosed with diabetes before surgery. In total, 111 patients (41.73%) had one or more postoperative complications, and there was no perioperative death. There was no statistically significant difference regarding postoperative complications between the diabetes group and the no diabetes group. Altogether, 120 patients (45.11%) experienced postoperative recurrence. Multivariate analysis revealed that diabetes was an independent risk factor for the recurrence (OR: 2.384, 95% CI: 1.065–5.336, p = 0.035), OS (HR: 1.597, 95% CI: 1.005–2.537, p = 0.047), and RFS (HR: 1.768, 95% CI: 1.068–2.925, p = 0.027) in AC patients after curative pancreatoduodenectomy. Conclusions: Diabetes may adversely affect the recurrence of patients with AC after curative pancreaticoduodenectomy, leading to an increased risk of poor prognosis in early-stage patients. Further studies involving a large sample size are needed to validate our results.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".