A197 USING FIB4 INDEX TO TRIAGE ELEVATED LIVER ENZYMES IN ASYMPTOMATIC PATIENTS WITHOUT VIRAL HEPATITIS
Bibliographic record
Abstract
Elevations in aminotransferases in asymptomatic patients is a common reason for referral to our tertiary care center. Current AASLD guidelines suggest that an extensive panel of biochemical workup should be performed for all of these patients, which is resource intensive and costly. The Fibrosis 4 (FIB4) Index for Liver Fibrosis is a scoring system validated for staging liver disease in HCV and NAFLD. We aim to use to use FIB4 to differentiate between high likelihood of NAFLD patients that would benefit from lifestyle changes, and low likelihood of NAFLD patients that would benefit from an extensive biochemical workup. An established database of consecutive referrals to our tertiary care center for elevated aminotransferases was accessed. The referrals were from both primary care physicians as well as other hepatologists, and extends from November 18th 2016 to August 9th, 2018. We sought to assess those individuals with a FIB4 index of <1.30 and had negative HBV/HCV status. Our primary outcome is the proportion of patients diagnosed with NAFLD. Secondary outcomes include the proportion of patients with abnormal biochemical workup that led to another diagnosis. 449 patients individual were identified using the criteria above. Out of those 449 patients, 14 had normal ALT and thus not included. Out of a total of 435 patients, 328 (75.4%) were male. Median age was 39 (IQR 32–46), and median BMI was 30.7 (IQR 28.0–35.2). 136 (31.3%) have never used alcohol or consumed <10 alcoholic drinks/week. Median FIB4 Index was 0.72 (0.55 – 0.92). Diagnosis at triage included NAFLD (89.0%), alcoholic liver disease (1.6%), mixed alcohol/NAFLD (7.6%), and others (1.8%). Two patients were diagnosed with PBC; no patients were diagnosed with hemochromatosis, Wilson’s disease, or alpha 1 anti-trypsin deficiency. Base on our cohort of patients, FIB4 is a helpful tool in identifying those who do not require an extensive biochemical workup on initial consultation. Persistent elevations in LFT despite adequate lifestyle changes for NAFLD would strongly warrant further investigations. None
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".