New-onset Diabetes After Distal Pancreatectomy
Bibliographic record
Abstract
OBJECTIVE: The true rate of new-onset diabetes (NODM) after distal pancreatectomy (DP) is not known. This systematic review was carried out to obtain exact percentages regarding the incidence of NODM after DP for different indications. BACKGROUND: Distal pancreatectomy is the standard procedure for removal of benign or (potentially) malignant lesions from the pancreatic body or tail and increasingly used for removal of often benign lesions. It is associated with low mortality rates, though postoperative diabetes remains a serious problem. METHODS: Embase, PubMed, Medline, Web of Science, the Cochrane Library, and Google Scholar were searched for articles reporting incidence of NODM after DP. Methodological quality of the included studies was assessed by means of the Newcastle-Ottawa scale for cohort studies and the Moga scale for case series. Mean weighted overall percentages of NODM after DP for different indications were calculated with 95% confidence intervals (CI) and corresponding P values. RESULTS: Twenty-six studies were included, comprising 1.731 patients undergoing DP. The average cumulative incidence of NODM after DP performed for chronic pancreatitis was 39% and for benign or (potentially) malignant lesions it was 14%. Comparing the proportions of these 2 groups showed a significant difference (95% CI: 0.351-0.434 and 0.110-0.172, respectively, P < 0.000). The average percentage of insulin-dependent diabetes among patients with NODM after DP was 77%. CONCLUSIONS: This review is the largest of its kind to assess the cumulative incidence of NODM after DP and shows that NODM is a frequently occurring complication, with incidence depending on the preexisting disease and follow-up time. Because NODM can affect quality of life, patients undergoing DP should be preoperatively provided with this information as specific as possible.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".