Clinical and biochemical features of diagnosed and undiagnosed patients with metabolic dysfunction-associated steatotic liver disease
Bibliographic record
Abstract
Background: The majority of the world's metabolic dysfunction-associated steatotic liver disease (MASLD) patient population remain undiagnosed. Whether the clinical and biochemical features of these individuals resemble those with an established diagnosis of MASLD remains to be determined. The aim of this study was to document and compare the demographics, associated metabolic comorbidities, liver biochemistry, and non-invasive markers of hepatic fibrosis and portal hypertension in diagnosed versus undiagnosed MASLD patients. Methods: The two study cohorts consisted of 3,101 MASLD patients attending a tertiary care centre (diagnosed) and 408 individuals with MASLD identified as a result of volunteering in a community-based MASLD screening clinic (undiagnosed). Results: <0.00001). BMIs were similar in the two cohorts. The prevalence and extent of liver transaminases (ALT and AST) and function test (albumin, bilirubin, and INR values) abnormalities were greater in diagnosed MASLD patients as were non-invasive determinants of hepatic fibrosis and portal hypertension (higher FIB-4 values and lower platelet counts, respectively). Conclusions: The demographics, metabolic co-morbidities, and severity of liver disease differ in diagnosed versus undiagnosed MASLD patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".