Trabecular and cortical bone density and architecture in women after 60 days of bed rest using high-resolution pQCT: WISE 2005
Bibliographic record
Abstract
Prolonged bed rest is used to simulate the effects of spaceflight and causes disuse-related loss of bone. While bone density changes during bed rest have been described, there are no data on changes in bone microstructure. Twenty-four healthy women aged 25 to 40 years participated in 60 days of strict 6-degree head-down tilt bed rest (WISE 2005). Subjects were assigned to either a control group (CON, n = 8), which performed no countermeasures; an exercise group (EXE, n = 8), which undertook a combination of resistive and endurance training; or a nutrition group (NUT, n = 8), which received a high-protein diet. Density and structural parameters of the distal tibia and radius were measured at baseline, during, and up to 1 year after bed rest by high-resolution peripheral quantitative computed tomography (HR-pQCT). Bed rest was associated with reductions in all distal tibial density parameters (p < 0.001), whereas only distal radius trabecular density decreased. Trabecular separation increased at both the distal tibia and distal radius (p < 0.001), but these effects were first significant after bed rest. Reduction in trabecular number was similar in magnitude at the distal radius (p = 0.021) and distal tibia (p < 0.001). Cortical thickness decreased at the distal tibia only (p < 0.001). There were no significant effects on bone structure or density of the countermeasures (p ≥ 0.057). As measured with HR-pQCT, it is concluded that deterioration in bone microstructure and density occur in women during and after prolonged bed rest. The exercise and nutrition countermeasures were ineffective in preventing these changes.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".