Italian Chronic Pancreatitis Registry (ITARECIPE): protocol for a nationwide cohort study
Bibliographic record
Abstract
INTRODUCTION: Chronic pancreatitis (CP) is a progressive inflammatory disease of the pancreas leading to permanent damage, resulting in both exocrine and endocrine insufficiency. Understanding the management of patients with CP and their outcomes is critical for improving patient care. CP is relatively rare in Italy and is characterised by various aetiologies and clinical progression requiring personalised treatment options. This registry (ITARECIPE) aims to prospectively collect and analyse data on patients with newly diagnosed CP to gain insights into its epidemiology, presentation, disease progression, and treatment outcomes. METHODS AND ANALYSIS: This is a multicentre, observational, non-interventional incident cohort study supported by the Italian Association for the Study of the Pancreas and endorsed by relevant Italian gastroenterological societies. ITARECIPE is the first registry in Italy focusing on newly diagnosed CP patients, leading to a comprehensive understanding of disease onset and progression. The study plans to enrol ≥300 patients annually over a minimum of 5 years. Data are recorded in a pseudo-anonymous electronic Case Report Form (eCRF) at baseline and follow-up visits, covering patient demographics, comorbidities, chronic medications, CP aetiology, pancreatic function (exocrine and endocrine), pain, complications, imaging, laboratory tests and treatments. It will track epidemiology, clinical history and treatment outcomes, potentially improving adherence to best practices and informing health policy decisions. The ITARECIPE registry will contribute significantly to the understanding of CP by providing detailed epidemiological, clinical and examinations data into disease management, which could help the development of future clinical practice and guidelines. ETHICS AND DISSEMINATION: The study was approved by the Ethics Committee (EC) of the promoter centre (San Raffaele Hospital, Milan, Italy; approval code 178/2022) and subsequently by the EC of each participating centre. All patients will be included after signing written informed consent and will be recorded in a pseudo-anonymous manner in a specific eCRF, in accordance with international principles and recommendations for observational studies. The ongoing results may be presented at national or international conferences and will be reported in peer-reviewed publications. TRIAL REGISTRATION NUMBER: NCT05733130.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".