Impact of Implementing a “FIB‐4 First” Strategy on a Pathway for Patients With NAFLD Referred From Primary Care
Bibliographic record
Abstract
Detection of advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is essential for stratifying patients according to the risk of liver‐related morbidity. Noninvasive methods such as vibration‐controlled transient elastography (VCTE) and Fibrosis‐4 index (FIB‐4) have been recommended to identify patients for further assessment. The aim of this study was to assess the potential impact of implementing a “FIB‐4 First” strategy to triage patients entering a NAFLD assessment pathway. The pathway for patients with suspected NAFLD was piloted at a tertiary liver center. Referral criteria were 16‐65 years old, elevated alanine aminotransferase and/or steatosis on imaging, and absence of a previous liver diagnosis. A registered nurse risk‐stratified all patients based on VCTE and FIB‐4 was calculated. Potential alternative diagnoses were excluded with bloodwork. A total of 565 patients underwent risk stratification with VCTE with a 97% success rate. Ten percent had VCTE of at least 8 kPa; 560 patients had FIB‐4 available for analysis and 87% had values less than 1.3. Of those with a FIB‐4 of at least 1.3, 69% had a VCTE less than 8 kPa. Further modeling showed that the presence of diabetes, age, and body mass index had only a moderate impact on the association between FIB‐4 and elastography values if using a FIB‐4 threshold of 1.3. Conclusion: A FIB‐4 threshold of 1.3 was acceptable for excluding the presence of advanced fibrosis (assessed by VCTE). A staged risk‐stratification model using FIB‐4 and VCTE could save up to 87% of further assessments. This model could improve accessibility by moving the initial fibrosis evaluation to the medical home and helping to prioritize patients for further specialized care.
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 | 0.000 | 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".