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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".