Postmortem survey of haemoglobin A1c, non-alcoholic steatohepatitis and liver fibrosis within a general population
Bibliographic record
Abstract
Aims Non-alcoholic steatohepatitis (NASH), fatty liver disease and fibrosis are associated with diabetes mellitus and obesity. Previous autopsy series have reported prevalence of fatty liver disease to be 11%–24%. Recent studies, using imaging and serology, suggest a prevalence of 20%–35%, NASH of 5% and advanced fibrosis of 2%–3%. We examined the prevalence of NASH and liver fibrosis in a general autopsy population. Methods A cross-sectional study of consecutive, adult, medicolegal autopsies over a 1-year period was conducted. Liver sections were scored for fibrosis, inflammation and steatosis using a modified NASH scoring system. Stepwise logistic regression was used to identify associations between NASH or moderate/severe fibrosis and several clinicopathological parameters, including postmortem haemoglobin A1c (HbA1c). Results Of 376 cases, 86 (22.9%) were classified as NASH. Prevalence of diabetes mellitus, body mass index (BMI) and postmortem HbA1c were significantly higher in NASH cases (39.5%, 32.3 kg/m2and 6.88%) than non-NASH cases (12.1%, 27.0 kg/m2and 5.73%). Decedents with moderate/severe fibrosis (6.9%) had higher prevalence of diabetes, BMI and HbA1c (50%, 31.4 kg/m2and 6.7%) compared with those with no/mild fibrosis (16%, 28 kg/m2and 5.9%). HbA1c ≥7% was found to be an independent predictor of NASH (OR 5.11, 95% CI 2.61 to 9.98) and advanced fibrosis (OR 3.94, 95% CI 1.63 to 9.53). Conclusions NASH and advanced fibrosis were higher in our general adult autopsy population compared with previously published estimates. This is a large series with histological evaluation showing that HbA1c >7.0% is independently associated with NASH and advanced 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".