Barriers and facilitators to liver fibrosis screening: Perspectives and practices of Canadian primary care physicians
Bibliographic record
Abstract
Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent, yet primary care physicians (PCPs) face barriers to identifying and referring high-risk individuals. We surveyed Canadian PCPs to identify barriers and facilitators to MASLD-related fibrosis screening. Methods: A multidisciplinary team developed a 38-item online survey with multiple-choice and Likert scale questions to assess PCPs' MASLD knowledge and barriers and facilitators to screening. The survey was distributed anonymously in April-August 2024. Results: Seventy-one participants completed the survey. One in five rated their MASLD diagnostic knowledge as very or extremely familiar, whereas a quarter reported little to no knowledge. Although >90% correctly identified obesity, type 2 diabetes, and dyslipidemia as risk factors, only 54% screened these populations. Among those who screened, 55% used FIB-4 and 29% transient elastography, while the most common tools were ultrasound (74%) and alanine aminotransferase (71%). Overall, 96% reported at least one barrier, including time limitations, resource constraints, and limited access to tools. Barriers also varied by province; PCPs in Alberta reported fewer access issues with tools like FIB-4 than those in other provinces, including Ontario and Quebec. Encouragingly, over 80% expressed willingness to adopt integrated guidelines and automated risk tools into their practice. Conclusions: Despite awareness of MASLD risk factors among Canadian PCPs, substantial gaps remain in screening due to limited knowledge, inconsistent tool use, and systemic barriers. These findings highlight the need for a coordinated national strategy to support PCPs in the early identification and referral of patients at risk for MASLD-related fibrosis.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".