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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".