The effects of loading and estrogen on rat bone growth
Bibliographic record
Abstract
This study evaluated the contributions of locomotive loading and estrogen to the development of diaphysis of rat femur. A randomized 2x2 study design was used. Altogether, 70 female Sprague-Dawley rats were used, of which 10 were euthanized at entry. Of the remaining rats, 16 served as controls, and the rest, 44, underwent a unilateral sciatic neurectomy. The effect of estrogen was removed by ovariectomizing one-half of the neurectomized rats. After 27 wk, the animals were euthanized, and the femora were excised. Irrespective of loading or estrogen, the femur length and mineral mass increased by 142 and 687%, respectively. Axial growth was not modulated either by locomotive loading or estrogen, but the loading resulted in direction-specific changes in the cross-sectional geometry. The estrogen-related gains were evident on the endocortical surface, while the loading-related gains occurred on the periosteal surface. The loading and estrogen were significantly associated with increased bone strength (21 and 15%, respectively) in the mediolateral direction, but not in the anteroposterior direction. Axial growth and accrual of bone mineral mass of the rat femur are largely independent of locomotive loading or estrogen, whereas these factors specifically account for the femur function, as either a mechanical lever or a mineral reservoir for reproduction, respectively.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".