Thawing of frozen calcaneus bone specimens has no effect on the bone mineral density using dual energy x-ray absorptiometry: a study in rabbits and humans
Bibliographic record
Abstract
The purpose of this paper is to study whether the deep freezing of bone and later thawing affects the bone mineral density (BMD) measurement. We used 56 calcanei from 28 adult female New Zealand white rabbits and 102 human calcanei from 51 donors post-mortem (27 men and 24 women, age 30-89). Dual energy x-ray absorptiometry evaluated BMD of the frozen specimen and of the thawed specimen. A main analysis compared BMD of each specimen in the frozen and thawed states. The mean BMD of 224 areas of frozen rabbits' calcanei was 0.31 +/- 0.08 g cm(-2) (95% confidence interval (CI) from 0.30 to 0.32 g cm(-2)) while the mean BMD of thawed rabbits' calcanei was 0.31 +/- 0.08 g cm(-2) (95% CI from 0.30 to 0.32 g cm(-2); paired t-test p > 0.01). The mean BMD of 306 areas of frozen human calcaneus was 0.73 +/- 0.22 g cm(-2) (95% CI from 0.70 to 0.76 g cm(-2)) while the mean BMD of thawed human calcaneus was 0.73 +/- 0.22 g cm(-2) (95% CI from 0.70 to 0.76 g cm(-2); p > 0.01). For both the rabbit and the human calcanei, a Bland-Altman analysis showed a mean difference between the BMD in the frozen and thawed states of 0.00 (limits of agreement, rabbit: -0.07 to 0.08 g cm(-2), human: -0.16 to 0.21 g cm(-2)). A high correlation was observed between calcaneus BMD in the frozen and thawed states (r = 0.94, 0.97, 0.92 and 0.99 respectively in all rabbit calcanei, all human calcanei, immobilized rabbit calcanei and osteopenic human calcanei, all p < 0.01). Bone mineral density is not affected by deep freezing and later thawing of the specimen. Therefore, the specimens need not be thawed to obtain valid and precise BMD measurement. These results are relevant to general musculoskeletal as well as osteoporosis research where the specimens undergo multiple tests in series.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".