Reduction in Proximal Femoral Strength in Patients With Acute Spinal Cord Injury
Bibliographic record
Abstract
Bone loss after spinal cord injury (SCI) is associated with an increased risk of fracture resulting from minor trauma. Proximal femoral fractures account for approximately 10% to 20% of the fractures in this population and are among the most serious of injuries. Our purpose was to quantify changes to proximal femoral strength in patients with acute SCI. Thirteen subjects received dual-energy X-ray absorptiometry (DXA) and clinical computed tomography (CT) scans at serial time points during acute SCI separated by a mean of 3.5 months (range 2.6 to 4.8 months). Areal bone mineral density (aBMD) at the proximal femur was quantified from DXA, and proximal femoral strength was predicted using CT-based finite element (FE) modeling in a sideways fall configuration. During the acute period of SCI, femoral neck and total proximal femur aBMD decreased by 2.0 ± 1.1%/month (p < 0.001) and 2.2 ± 0.7%/month (p < 0.001), respectively. The observed reductions in aBMD were associated with a 6.9 ± 2.0%/month (p < 0.001) reduction in femoral strength. Thus, changes in femoral strength were some 3 times greater than the observed changes in aBMD (p < 0.001). It was interesting to note that in just 3.5 months of acute SCI, reductions in strength for some patients were on the order of that predicted for lifetime declines owing to aging. Therefore, it is important that therapeutic interventions are implemented soon after SCI in an effort to halt bone loss and decrease fracture risk. In addition, clinicians utilizing DXA to monitor bone health after SCI should be aware of the potential discrepancy between changes in aBMD and strength.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".