Computed Tomography Measurement of Hepatic Steatosis: Prevalence of Hepatic Steatosis in a Canadian Population
Bibliographic record
Abstract
Background/Aims. Nonalcoholic fatty liver disease (NAFLD) is a chronic liver disease that can progress to cirrhosis and hepatocellular carcinoma. This retrospective chart review investigated the incidence of hepatic steatosis in London, Ontario, Canada. Methods. A retrospective chart review was performed on emergency room (ER) patients undergoing nonscheduled computed tomography (CT) imaging over a six-month period in London, Ontario. CT images and reports were examined to determine presence of steatosis. Analyses of the electronic chart for a period of six months following the CT and communication with the patients' family doctors were used to determine if there was follow-up. Waist circumference, subcutaneous fat depth, and abdominal fat volumes were calculated. Results. 48/450 patients meeting inclusion criteria were identified by radiology as having steatosis, with 34/40 (85%) family physicians unaware of the finding. 24.7% (100/405) of patients met standard CT criteria for steatosis, 40 of which were reported by the radiologist. Waist circumference, subcutaneous adipose tissue depth, subcutaneous adipose tissue volume, and visceral adipose tissue volume were significantly associated with steatosis. Conclusions. The hepatic steatosis prevalence we report is the first reported in a Canadian population. Early identification of steatosis will become more important as new pharmacologic therapies arise.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".