Rapid Small-Animal Dual-Energy X-Ray Absorptiometry Using Digital Radiography
Bibliographic record
Abstract
Although dual-energy X-ray absorptiometry (DEXA) is an established technique for clinical assessment of areal bone mineral density (BMD), the spatial resolution, signal-to-noise ratio, scan time, and availability of clinical DEXA systems may be limiting factors for small-animal investigations using a large number of specimens. To avoid these limitations, we have implemented a clinical digital radiography system to perform rapid area DEXA analysis on in vitro rat bone specimens. A crossed step-wedge (comprised of epoxy-based materials that mimic the radiographic properties of tissue and bone) was used to calibrate the system. Digital radiographs of bone specimens (pelvis, spine, femur, and tibia from sham-ovariectomized [SHAM] and ovariectomized [OVX] rats) were obtained at 40 kilovolt peak (kVp) and 125 kVp, and the resulting areal BMD values were compared with those obtained with a clinical fan-beam DEXA system (Hologics QDR 4500). Our investigation indicates that the cross-wedge calibrated (CWC) DEXA technique provides high-precision measurements of bone mineral content (BMC; CV = 0.6%) and BMD (CV = 0.8%) within a short acquisition time (<30 s). Areal BMD measurements reported by the CWC-DEXA system are within 8.5% of those reported by a clinical fan-beam scanner, and BMC values are within 5% of the known value of test specimens. In an in vivo application, the CWC-DEXA system is capable of reporting significant differences between study groups (SHAM and OVX) that are not reported by a clinical fan-beam DEXA system, because of the reduced variance and improved object segmentation provided by the CWC-DEXA system.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".