Protocol for measurement of liver fat by computed tomography
Bibliographic record
Abstract
To develop a protocol for measurement of liver fat using computed tomography (CT), we conducted a preliminary study with 118 men and 76 women to determine a readily identifiable vertebral landmark at which the CT image displayed both liver and spleen. Analysis of five landmarks revealed that the CT image obtained at the T12-L1 level simultaneously displayed the liver and spleen in 90% of the men and women. The T12-L1 protocol was cross-validated on a sample of 130 men and 113 women. In this sample, we also assessed the regional characteristics of liver and spleen tissue attenuation at the T12-L1 level by subdividing each image into quartiles from anterior to posterior, each of which were further divided into medial and lateral regions. A similar analysis was performed on images located 12 mm above and below T12-L1. The T12-L1 image displayed both liver and spleen in 92% (403 of 437) of the combined study sample. There was a significant (P<0.005) stepwise increase in attenuation values [Hounsfield units (HU)] from the inferior to superior image. Although some significant (P<0.05) differences were observed between the eight regions by comparison to the whole liver or spleen, the average magnitude of the difference was <2.0 HU for liver and <3.5 HU for spleen. Acquisition of a single CT image at the T12-L1 level is a practical and reliable method for routine measurement of liver fat in research and clinical settings.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.026 |
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".