Efficacy and Safety of a New Formulation of Pancrelipase (Ultrase MT20) in the Treatment of Malabsorption in Exocrine Pancreatic Insufficiency in Cystic Fibrosis
Bibliographic record
Abstract
Background. Pancreatic enzyme replacement therapy is the standard of care for treatment of malabsorption in patients with cystic fibrosis (CF) and exocrine pancreatic insufficiency (PI). Aim. To evaluate efficacy and safety of a new formulation of pancrelipase (Ultrase MT20) in patients with CF and PI. Coefficients of fat absorption (CFA%) and nitrogen absorption (CNA%) were the main efficacy parameters. Safety was evaluated by monitoring laboratory analyses, adverse events (AEs), and overall signs and symptoms. Methods. Patients (n = 31) were randomized in a crossover design comparing this pancrelipase with placebo during 2 inpatient evaluation periods (6-7 days each). Fat and protein/nitrogen ingestion and excretion were measured from food diaries and 72-hour stool collections. CFA% and CNA% were calculated for each period and compared. Results. Twenty-four patients provided analyzable data. This pancrelipase increased mean CFA% and CNA% (+34.7% and +25.7%, resp., P < .0001 for both), reduced stool frequency, and improved stool consistency compared with placebo. Placebo-treated patients reported more AEs, with gastrointestinal symptoms being the most frequently reported AE. Conclusions. This pancrelipase is a safe and effective treatment for malabsorption associated with exocrine PI in patients with CF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".