Exocrine pancreatic insufficiency after esophagectomy: a systematic review of literature
Bibliographic record
Abstract
Complaints of maldigestion, malabsorption, and unintended weight loss after esophagectomy are often attributed to an impaired exocrine pancreatic function. This review systematically summarizes all literature reporting on the presence of exocrine pancreatic insufficiency (EPI) after esophagectomy and the effect of treatment with pancreatic enzymes on gastrointestinal complaints, body weight, and quality of life. Databases of PubMed, Embase, and Wiley/Cochrane Library were searched systematically until July 2020. Studies reporting on EPI and pancreatic enzyme replacement therapy after esophagectomy were included. The Newcastle-Ottawa scale was used to assess study quality. Four studies, including 158 patients, were selected. The maximum score for study quality was six (range 4-6). Exocrine pancreatic function was investigated in three studies, measured by fecal elastase-1 and 72-hour fecal fat excretion. Fecal elastase-1 levels <200 μg/g were reported in 16% of patients at 4 months, 18% at 6 months, and 31% at 18-24 months postoperatively. A decreased fecal fat absorption was noticed in 57% 1 month postoperatively. Treatment with pancreatic enzymes was reported in two studies. In patients with fecal elastase-1 levels <200 μg/g, 90% of patients reported improvement in symptoms and 70% reported improvement in weight. In patients with complaints of steatorrhea, 87% noticed settlement of symptoms. Based on current literature, complaints of maldigestion, malabsorption, and unintended weight loss after esophagectomy are common and can be related to an impaired exocrine pancreatic function. High-quality studies evaluating the presence of EPI and the effect of treatment with pancreatic enzymes after esophagectomy are needed to verify this conclusion.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".