Delayed gastric emptying following pancreaticoduodenectomy: Incidence, risk factors, and healthcare utilization
Bibliographic record
Abstract
AIMTo characterize incidence and risk factors for delayed gastric emptying (DGE) following pancreaticoduodenectomy and examine its implications on healthcare utilization. METHODSA prospectively-maintained database was reviewed.DGE was classified using International Study Group of Pancreatic Surgery criteria.Patients who developed DGE and those who did not were compared. RESULTSTwo hundred and seventy-six patients underwent pancreaticoduodenectomy (PD) (> 80% pyloruspreserving, antecolic-reconstruction). DGE developed in 49 patients (17.8%): 5.1% grade B, 3.6% grade C. Demographic, clinical, and operative variables were similar between patients with DGE and those without.DGE patients were more likely to present multiple Retrospective Study 74 March 27, 2017|Volume 9|Issue 3| WJGS|www.wjgnet.comMohammed S et al .DGE after Whipple procedure complications (32.6% vs 4.4%, ≥ 3 complications, P < 0.001), including postoperative pancreatic fistula (POPF) (42.9% vs 18.9%, P = 0.001) and intra-abdominal abscess (IAA) (16.3% vs 4.0%, P = 0.012).Patients with DGE had longer hospital stay (median, 12 d vs 7 d, P < 0.001) and were more likely to require transitional care upon discharge (24.5% vs 6.6%, P < 0.001).On multivariate analysis, predictors for DGE included POPF [OR = 3.39 (1.35-8.52),P = 0.009] and IAA [OR = 1.51 (1.03-2.22),P = 0.035].CONCLUSION Although DGE occurred in < 20% of patients after PD, it was associated with increased healthcare utilization.Patients with POPF and IAA were at risk for DGE.Anticipating DGE can help individualize care and allocate resources to high-risk patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".