High resolution <i>in vivo</i> micro‐computed tomography is preferential over dual energy X‐ray absorptiometry for detecting bone loss in the orchidectomized guinea pig
Bibliographic record
Abstract
Monitoring longitudinal skeletal changes in small animals requires sensitive and precise imaging tools. This study compares two in vivo imaging modalities, dual energy X‐ray absorptiometry (DXA) and micro‐computed tomography (μCT) for assessing bone quality in the guinea pig. Middle‐aged male guinea pigs (n=40; 70 wks) were randomized into orchidectomized (ORX) or SHAM groups for 16 weeks. At termination, femurs were scanned in vivo using DXA (DXA in vivo , QDR 4500A, Hologic). Excised bones were scanned again using DXA (DXA ex vivo ), and μCT (LCT‐200, Aloka). Bone mineral content (BMC) was assessed by ashing. Independent t test was used to compare differences in SHAM vs ORX; and agreement between methods assessed with Bland‐ Altman plots. Substantial bias (7–60%) was observed in all measured variables except areal bone mineral density (aBMD). As shown by DXA in vivo and μCT, whole bone aBMD was significantly higher in SHAM vs ORX. DXA in vivo also suggested higher BMC but smaller bone in SHAM vs ORX, such differences were not observed in DXA ex vivo , μCT or ashing; indicating the lower aBMD in ORX observed in DXA in vivo is likely an artifact resulting from variations in nearby tissues and positioning. Also, compartmental changes in volumetric BMD and bone volume fraction shown by μCT are masked if only DXA was used. These data suggest that μCT is a more sensitive and reliable tool for capturing skeletal changes in the guinea pig.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".