Mapping global new-onset, worsening, and resolution of diabetes following partial pancreatectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Partial pancreatectomy, commonly used for chronic pancreatitis, or pancreatic lesions, has diverse impacts on endocrine and metabolism system. The study aims to determine the global prevalence of new-onset, worsening, and resolution of diabetes following partial pancreatectomy. METHODS: The authors searched PubMed, Embase, Web of Science, and Cochrane Library from inception to October, 2023. DerSimonian-Laird random-effects model with Logit transformation was used. Sensitivity analysis, meta-regression, and subgroup analysis were employed to investigate determinants of the prevalence of new-onset diabetes. RESULTS: A total of 82 studies involving 13 257 patients were included. The overall prevalence of new-onset diabetes after partial pancreatectomy was 17.1%. Univariate meta-regression indicated that study size was the cause of heterogeneity. Multivariable analysis suggested that income of country or area had the highest predictor importance (49.7%). For subgroup analysis, the prevalence of new-onset diabetes varied from 7.6% (France, 95% CI: 4.3-13.0) to 38.0% (UK, 95% CI: 28.2-48.8, P <0.01) across different countries. Patients with surgical indications for chronic pancreatitis exhibited a higher prevalence (30.7%, 95% CI: 21.8-41.3) than those with pancreatic lesions (16.4%, 95% CI: 14.3-18.7, P <0.01). The type of surgical procedure also influenced the prevalence, with distal pancreatectomy having the highest prevalence (23.7%, 95% CI: 22.2-25.3, P <0.01). Moreover, the prevalence of worsening and resolution of preoperative diabetes was 41.1 and 25.8%, respectively. CONCLUSIONS: Postoperative diabetes has a relatively high prevalence in patients undergoing partial pancreatectomy, which calls for attention and dedicated action from primary care physicians, specialists, and health policy makers alike.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".