Global research initiative for patient screening on MASH (GRIPonMASH) protocol: rationale and design of a prospective multicentre study
Bibliographic record
Abstract
INTRODUCTION: The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) may be as high as 38% in the adult population with potential serious complications, multiple comorbidities and a high socioeconomic burden. However, there is a general lack of awareness and knowledge about MASLD and its progressive stages (metabolic dysfunction-associated steatohepatitis (MASH) and fibrosis). Therefore, MASLD is still far underdiagnosed. The 'Global Research Initiative for Patient Screening on MASH' (GRIPonMASH) consortium focuses on this unmet public health need. GRIPonMASH will help (primary) healthcare providers to implement a patient care pathway, as recommended by multiple scientific societies, to identify patients at risk of severe MASLD and to raise awareness. Furthermore, GRIPonMASH will contribute to a better understanding of the pathophysiology of MASLD and improved identification of diagnostic and prognostic markers to detect individuals at risk. METHODS: This is a prospective multicentre observational study in which 10 000 high-risk patients (type 2 diabetes mellitus, obesity, metabolic syndrome or hypertension) will be screened in 10 European countries using at least two non-invasive tests (Fibrosis-4 index and FibroScan). Blood samples and liver biopsy material will be collected and biobanked, and multiomics analyses will be conducted. ETHICS AND DISSEMINATION: The study will be conducted in compliance with this protocol and applicable national and international regulatory requirements. The study initiation package is submitted at the local level. The study protocol has been approved by local medical ethical committees in all 10 participating countries. Results will be made public and published in scientific, peer-reviewed, international journals and at international conferences. REGISTRATION DETAILS: NCT05651724, registration date: 15 Dec 2022.
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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 | 0.110 | 0.090 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.017 |
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".