Leg Bone Geometry in Human Diabetic Neuropathy
Bibliographic record
Abstract
Bone geometry is an important indicator of bone health and fracture risk, but has not been studied in individuals with diabetic neuropathy (DN). The objective was to investigate the effects of DN on tibial cortical and medullary cross‐sectional areas (CSA) using magnetic resonance imaging. Sequential images of 1mm thick slices were acquired of the right leg from the tibial plateau to the talus in 6 individuals diagnosed with DN and 8 age‐matched (32 to 79 y) controls. The CSA (cm 2 ) was measured (average of 2 adjacent slices) at 3 sites, 20% (proximal), 50% (middle) and 80% (distal) of tibial length, by a blinded analyzer. At the proximal site only, medullary CSA in DN was significantly greater than controls (means + SD: 6.1±1.4 vs. 4.6±0.4). Also, as a percent of total CSA, DN compared with controls had significantly less cortical (~30% vs. ~38%) and greater medullary (~69% vs. ~62%) areas. At middle and distal sites there were no differences in any measures. These preliminary results indicate bone geometry is negatively affected by DN at the proximal tibia. This may be due to lower weight bearing or mechanical loading than at the middle or distal aspects. Presumed lower levels of physical activity in DN coupled with less muscle mass and strength, but heavier body weights, may be important factors influencing bone geometry to consider in future studies, including assessment of the lesser weight‐bearing fibula. Supported by NSERC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".