Clinical Trials in Pancreatitis: Opportunities and Challenges in the Design and Conduct of Patient-Focused Clinical Trials in Recurrent Acute and Chronic Pancreatitis
Bibliographic record
Abstract
ABSTRACT: Recurrent acute pancreatitis and chronic pancreatitis represent high morbidity diseases, which are frequently associated with chronic abdominal pain, pancreatic insufficiencies, and reduced quality of life. Currently, there are no therapies to reverse or delay disease progression, and clinical trials are needed to investigate potential interventions that would address this important gap. This conference report provides details regarding information shared during a National Institute of Diabetes and Digestive and Kidney Diseases-sponsored workshop on Clinical Trials in Pancreatitis that sought to clearly delineate the current gaps and opportunities related to the design and conduct of patient-focused trials in recurrent acute pancreatitis and chronic pancreatitis. Key stakeholders including representatives from patient advocacy organizations, physician investigators (including clinical trialists), the US Food and Drug Administration, and the National Institutes of Health convened to discuss challenges and opportunities with particular emphasis on lessons learned from trials in participants with other painful conditions, as well as the value of incorporating the patient perspective throughout all stages of trials.
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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 | 0.894 | 0.817 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.034 | 0.028 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.022 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".