Rapid in vivo whole body composition of rats using cone beam μCT
Bibliographic record
Abstract
Precise, noninvasive analysis and quantification of in vivo body composition is essential for research involving longitudinal, small-animal disease models. We investigated the feasibility and precision of a rapid, flat-panel μCT scanner to report whole body adipose tissue volume (ATV), lean tissue volume (LTV), skeletal tissue volume (STV), and bone mineral content (BMC) in 25 postmortem female and 52 live male Sprague-Dawley rats. μCT images, acquired in three 90-mm segments and reconstructed with 308 μm of isotropic voxel spacing, formed contiguous image volumes of each entire rat specimen. Three signal-intensity thresholds (determined to be -186, 5, and 155 HU) were used to classify each voxel as adipose, lean, or skeletal tissue, respectively. Tissue masses from the volume fractions of ATV, LTV, and STV were calculated from assumed tissue densities of 0.95, 1.05, and 1.92 g/cm(-3), respectively. A CT-derived total mass was calculated for each rat and compared with the gravimetrically measured mass, which differed on average for the postmortem female and the live male group by 2.5 and 1.1%, respectively. To evaluate the accuracy of the CT-derived body composition technique, following the live male study excised muscle tissue in the lower right leg of all rats in group B were compared with the image-derived LT measurement of the same regional compartment and found to differ on average by 2.2%. Through repeated CT measurements of postmortem specimens, the whole body ATV, LTV, STV, and BMC measurement analysis gave a precision value of ±0.6, 1.9, 1.7, and 0.5% of the average value, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".