Incidence and risk factors for new-onset diabetes mellitus (NODM) in post-pancreatectomy patients diagnosed with pancreatic adenocarcinoma (PAC): A systematic review
Bibliographic record
Abstract
BACKGROUND: Pancreatic adenocarcinoma is a highly aggressive malignancy often requiring pancreatectomy as part of curative treatment. However, pancreatectomy frequently leads to endocrine dysfunction, such as new-onset diabetes mellitus (NODM). But the relationship between post pancreatectomy NODM and pancreatic carcinoma and the relevant risk factors remains underexplored. METHODS: In accordance with the PRISMA guidelines, a systematic search for pertinent studies was conducted across electronic databases including MEDLINE, Cochrane, EMBASE, and Scopus, covering the period from January 2000 to March 2025. The quality of these studies was evaluated using the Newcastle-Ottawa Scale, specifically designed for cohort studies. Subgroup analysis was done in terms of different pancreatectomy procedures. RESULTS: 45 quantitative studies were analysed, of which 16 (35.5%) were prospective studies and 29 (64.5%) were retrospective studies. Regarding the subgroup analysis, 11 studies analysed Pancreatico-Duodenectomy (PD) alone, another 12 studies analysed Distal Pancreatectomy (DP) alone, and the rest of the 22 studies compared PD with DP. The overall incidence of NODM was 24.5%, with the PD group incidence being 23.2%, and the DP group incidence was 26.3%. Older age, High BMI, preop hyperglycemia, pre-op high HbA1c, pre-existing chronic pancreatitis, low remnant pancreatic volume and post-operative complications were associated with a high incidence of NODM. CONCLUSIONS: The development of NODM after partial pancreatic resections for pancreatic adenocarcinoma is a severe complication that requires prompt diagnosis, careful monitoring and systematic management. Hence, healthcare professionals should have detailed knowledge of the surgical procedure and its potential for diabetes complications postoperatively, using risk factor assessment.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".