The effect of ovariectomy combined with hindlimb unloading and reloading on the long bones of mature Sprague-Dawley rats
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of loss of ovarian function and mechanical loading (ie, inactivity) alone or in combination on bone mass and strength. DESIGN: Mature (aged 6 mo) rats were ovariectomized to induce loss of ovarian function and bone. Hindlimb unloading (HLU) was used to determine the effect of mechanical unloading and reloading on bone mass and strength. Bone mass of the femur and tibia was determined using dual-energy x-ray absorptiometry. Femoral and tibial bone strength was determined by a three-point bending test and by a torsion test. RESULTS: Ovariectomy (OVX) alone decreased total bone mineral density (BMD) in the femur (-5.5%, P=0.03) and tibia (-7.3%, P=0.01) compared with that for sham-operated animals. HLU alone for 4 weeks had no significant effect on bone. Together OVX/HLU accentuated BMD loss in the femur (-10.5%, P<0.01) compared with that for sham-operated animals. The femur was more sensitive than the tibia to the combination of OVX/HLU, indicated by the reduction (-5.3%, P<0.05) of total BMD below that achieved by OVX alone. Torsion tests showed that OVX/HLU but not OVX or HLU alone reduced bone strength. There was a correlation between lower femoral total BMD (r2=0.65, P<0.001) and reduced torque strength. Bone loss did not continue during the 2 weeks of reloading. CONCLUSIONS: OVX accompanied by mechanical unloading results in more rapid and severe bone loss than either OVX or unloading alone and therefore is associated with a greater likelihood of osteoporosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".