Evidence-based Guidelines for the Management of Exocrine Pancreatic Insufficiency After Pancreatic Surgery
Bibliographic record
Abstract
OBJECTIVE: To provide evidence-based recommendations for the management of exocrine pancreatic insufficiency (EPI) after pancreatic surgery. BACKGROUND: EPI is a common complication after pancreatic surgery but there is certain confusion about its frequency, optimal methods of diagnosis, and when and how to treat these patients. METHODS: Eighteen multidisciplinary reviewers performed a systematic review on 10 predefined questions following the GRADE methodology. Six external expert referees reviewed the retrieved information. Members from Spanish Association of Pancreatology were invited to suggest modifications and voted for the quantification of agreement. RESULTS: These guidelines analyze the definition of EPI after pancreatic surgery, (one question), its frequency after specific techniques and underlying disease (four questions), its clinical consequences (one question), diagnosis (one question), when and how to treat postsurgical EPI (two questions) and its impact on the quality of life (one question). Eleven statements answering those 10 questions were provided: one (9.1%) was rated as a strong recommendation according to GRADE, three (27.3%) as moderate and seven (63.6%) as weak. All statements had strong agreement. CONCLUSIONS: EPI is a frequent but under-recognized complication of pancreatic surgery. These guidelines provide evidence-based recommendations for the definition, diagnosis, and management of EPI after pancreatic surgery.
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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 | 0.040 | 0.167 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.020 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".