Systemic bone loss during fracture healing: new evidence from HR-pQCT analyses
Bibliographic record
Abstract
A major risk factor for future fractures is a history of previous fractures. It has been reported that fractures occurring before the age of 20 are only predictive of future fractures in men, but not women, whereas fractures after that age are associated with an increased risk of re-fracture in both sexes.1,2 The reasons for re-fracture may include persisting risk factors that were responsible for the initial fracture, altered mechanical environment, or bone loss occurring after a fracture. Bone loss following fracture occurs not only in the affected bone but also systemically in other bones.3,4 Systemic increases in bone remodeling, so-called regional and systemic acceleratory phenomenon, occur during bone healing, whereby increased resorption might lead to systemic bone loss in humans and mice after fracture.5–7 The underlying mechanisms responsible for systemic bone loss after fracture have yet to be elucidated, although it is thought that inflammation or disuse may be involved. While chronic inflammation is associated with bone loss, the effect of acute inflammation after fracture on systemic bone loss is unclear. Bone adapts to its mechanical environment; during bed rest or microgravity in space flight, bone resorption outpaces formation, resulting in net bone loss. Thus, general lack of activity and disuse of the injured limb after a fracture likely contribute to the local and systemic bone loss. Systemic bone loss may also be a consequence of mineral being transported to the site of injury to aid in fracture callus formation. There is evidence suggesting that calcium and vitamin D intake after a fracture can reduce subsequent systemic bone loss.8,9
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".