Use of non‐invasive diagnostic tools for metabolic dysfunction‐associated steatohepatitis: A qualitative exploration of challenges and barriers
Bibliographic record
Abstract
BACKGROUND AND AIMS: Non-invasive tests (NITs) are underutilized for diagnosis and risk stratification in metabolic dysfunction-associated steatotic liver disease (MASLD), despite good accuracy. This study aimed to identify challenges and barriers to the use of NITs in clinical practice. METHODS: We conducted a qualitative exploratory study in Germany, Italy, United Kingdom and United States. Phase 1 participants (primary care physicians, hepatologists, diabetologists, researchers, healthcare administrators, payers and patient advocates; n = 29) were interviewed. Phase 2 participants (experts in MASLD; n = 8) took part in a group discussion to validate and expand on Phase 1 findings. Finally, we triangulated perspectives in a hybrid deductive/inductive thematic analysis. RESULTS: Four themes hindering the use of NITs emerged: (1) limited knowledge and awareness; (2) unclear referral pathways for patients affected by liver conditions; (3) uncertainty over the value of NITs in monitoring and managing liver diseases; and (4) challenges justifying system-level reimbursement. Through these themes, participants perceived a stigma associated with liver diseases, and primary care physicians generally lacked awareness, adequate knowledge and skills to use recommended NITs. We identified uncertainties over the results of NITs, specifically to guide lifestyle intervention or to identify patients that should be referred to a specialist. Participants indicated an ongoing need for research and development to improve the prognostic value of NITs and communicating their cost-effectiveness to payers. CONCLUSIONS: This qualitative study suggests that use of NITs for MASLD is limited due to several individual and system-level barriers. Multi-level interventions are likely required to address these barriers.
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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 | 0.034 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".