Bone Resorption Markers and Dual‐Energy<scp>X</scp>‐Ray Absorptiometry in Dogs with Avascular Necrosis, Degenerative Joint Disease, and Trauma of the Coxofemoral Joint
Bibliographic record
Abstract
OBJECTIVES: To compare the ability of N-terminal telopeptide (NTx) assays and dual-energy x-ray absorptiometry (DEXA) to detect bone resorption in dogs with nonneoplastic bone lysis and evaluate the correlation between these diagnostic tools. STUDY DESIGN: Prospective, cross-sectional clinical study. ANIMALS: Dogs (n = 35; 39 femoral heads) that had femoral head and neck ostectomy and 6 cadaver specimens from healthy immature small dogs. METHODS: Small dogs with avascular necrosis (n = 12), a reference group of small dogs (7), large dogs with degenerative joint disease (DJD; 10), and large dogs with trauma (10) were studied in addition to 6 femoral heads harvested from 6 small immature and healthy dogs euthanatized for reasons unrelated to this study. Densitometric measurements of femoral heads, urine NTx excretion, and serum NTx concentration were compared between groups. RESULTS: Avascular necrosis resulted in a decrease in bone mineral density (BMD) (0.18 ± 0.01 g/cm(2;) P < .01) of the femoral head and elevation of serum NTx (159.3 ± 59.4 nM; P = .03) compared to small dog controls (0.28 ± 0.02 g/cm(2) ; 18.7 ± 1.83 nM, respectively), but did not seem to affect urine NTx. DJD in large dogs did not seem to affect any of the densitometric parameters evaluated. BMD (P = .03) and serum NTx (P = .04) were lower in small compared to large dogs. Serum NTx and densitometric measurements correlate inversely with each other (P = .001) but neither test correlated with urine NTx (P = .8-.9). CONCLUSION: Serum NTx levels vary with dog size but seem to correlate better with BMD better than urine NTx excretion in dogs with nonneoplastic bone resorption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".