Longitudinal assessment of bone loss using quantitative ultrasound in a blood‐induced arthritis rabbit model
Bibliographic record
Abstract
INTRODUCTION: Osteoporosis is common in haemophilic arthropathy. Quantitative ultrasound (QUS) can be a suitable alternative for dual-energy x-ray absorptiometry for diagnosing osteoporosis in haemophiliacs due to its lack of ionizing radiation, and ease to use. AIM: We investigated the intra- and inter-operator reliability of QUS, its responsiveness to bone growth, its ability to differentiate bone adjacent to blood-injected vs. control joints, and the effect of soft tissues on the speed of sound (SOS) QUS values in a juvenile white New Zealand rabbit model of blood-induced arthritis. METHODS: Eight of 16 rabbits were injected with autologous blood (0.1 mL kg(-1) ) 8 times over a 17-week period, the remaining eight rabbits served as controls. SOS was measured at baseline, weeks 8 and 17 in vivo and after the bones were excised on week 17. RESULTS: Intra- and inter-operator coefficients of variation for QUS data were <5% and intraclass correlation coefficients were >60% for 22/27 (81.5%) of bones assessed. The level of interval increase in SOS values from baseline to week 17 was significantly different in tibiae of injected, contralateral to injected and non-injected knee groups by anova (P = 0.01). In vivo (mean ± SD, 4147.17 ± 96.27 m s(-1) ) and postmortem (4457.85 ± 104.00 m s(-1) ) measurements on week 17 differed (P < 0.01) indicating an effect of soft tissues on SOS. CONCLUSION: In conclusion, QUS' acceptable reliability, its responsiveness to growth-related changes and its ability to discriminate injected and non-injected joints make this technique a plausible candidate as a diagnostic tool for osteoporosis in the paediatric haemophilic population if these results are confirmed upon animal-human translation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".