Assessing bone mineral density in vivo: quantitative computed tomography
Bibliographic record
Abstract
Egg-laying hens require substantial amounts of Ca to support eggshell formation. Over time, structural bone is catabolized to provide some of the Ca required; the structural bone is not replaced. As the hen ages, this can eventually lead to osteoporosis. Quantitative Computed Tomography (QCT) is a nondestructive technique used to measure bone mineral density (BMD). QCT is used diagnostically in humans to assess osteoporosis; BMD determined by QCT is correlated with other, more invasive methods of bone mineral determinations, such as ashing. An x-ray is sent through a bone at multiple angles within a plane to generate a 2-dimensional image and a 3-dimensional calculation of volume and BMD. The technique allows resolution of total, trabecular, and cortical BMD and cross-sectional areas. The separation of bone types allows very precise measurements of the bone compartments most important in Ca supply for eggshell formation and bone strength. QCT has been adapted in our laboratory to measure BMD in vivo and ex vivo in poultry; values obtained for poultry bones are moderately correlated with destructive means of assessing bone quality such as breaking strength, ashing, and chemical bone mineral determinations. Thus, changes in BMD of individual birds can be measured over time; BMD at specific time points can be correlated with production parameters and eggshell quality traits. QCT is an effective technique to measure BMD in laying hens, which allows resolution of total BMD as well as cortical and trabecular BMD.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".