The EFFect of dietary fat content on the recurrence of pancreaTitis (EFFORT): Protocol of a multicenter randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Around 20% of patients with acute pancreatitis (AP) will develop acute recurrent pancreatitis (ARP) and 10% will progress to chronic pancreatitis. While interventions to avoid recurrences exist for the two most common causes - abstinence for alcoholic and cholecystectomy for biliary pancreatitis - the are no known preventive measures in idiopathic ARP. Though it is not included in any of the guidelines, a low-fat diet is often recommended. Our aim is to test dietary fat reduction's effect on AP recurrence in a randomized controlled setting, in order to provide high-quality evidence for the validity of such an intervention. METHODS, DESIGN: Participants with at least 2 episodes of AP in the preceding 2 years of which the last episode was idiopathic will be randomized to one of two diets with different fat contents: a 'reduced fat diet' (15% fat, 65% carbohydrate, 20% protein) and a 'standard healthy diet' (30% fat, 50% carbohydrate, 20% protein; based on WHO recommendations). Participants will be followed-up for 2 years (visits will be scheduled for months 3, 6, 12, 18 and 24) during which they will receive a repeated session of nutritional guidance, complete food frequency questionnaires and data on relapse, mortality, BMI, cardiovascular parameters and serum lipid values will be collected. DISCUSSION: This study will determine the effect of modifying the dietary fat content on AP recurrence, mortality, serum lipids and weight loss in idiopathic cases.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".