Controlled Attenuation Parameter (CAP): a noninvasive method for the detection of hepatic steatosis based on transient elastography
Bibliographic record
Abstract
BACKGROUND: Accurate tools for the noninvasive detection of hepatic steatosis are needed. The Controlled Attenuation Parameter (CAP) specifically targets liver steatosis using a process based on transient elastography. METHODS: Patients with chronic liver disease and body mass index (BMI) ≥28 kg/m(2) underwent biopsy and liver stiffness measurement (LSM) with simultaneous CAP determination using the FibroScan(®) M probe. The performance of the CAP for diagnosing steatosis compared with biopsy was assessed using areas under receiver operating characteristic curves (AUROC). RESULTS: A total of 153 patients were included: 69% were male, median BMI was 32 kg/m(2); 47% had nonalcoholic fatty liver disease (NAFLD); and 65% had significant (≥10%) steatosis. The CAP was significantly correlated with the percentage of steatosis (ρ = 0.47) and steatosis grade (ρ = 0.51; both P < 0.00005). The median CAP was higher among patients with significant steatosis (317 [IQR 284-339] vs. 250 [227-279] dB/m with <10% steatosis; P < 0.0005) and the AUROC for this outcome was 0.81 (95% CI 0.74-0.88). At a cut-off of 283 dB/m, the CAP was 76% sensitive, 79% specific, and had positive and negative predictive values of 87% and 64%, respectively. CAP performance was not influenced by measurement variability, but was higher in patients with mild (F0-F1) fibrosis (AUROC 0.89 vs. 0.72 with F2-F4; P = 0.03). The AUROCs of the CAP for ≥5%, >33% and >66% steatosis were 0.79, 0.76 and 0.70, respectively. CONCLUSIONS: The CAP is a promising tool for the noninvasive detection of hepatic steatosis. Advantages of CAP include its ease of measurement, operator-independence and simultaneous availability with LSM for fibrosis assessment.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".