Differences and similarities in cortical bone of the femur between donors with and without type 2 diabetes
Bibliographic record
Abstract
For a given BMD, adults with type 2 diabetes (T2D) have greater fracture risk than adults without the disease. To test the hypothesis that T2D lowers the fracture resistance of human cortical bone by negatively altering the bone matrix quality, we acquired cadaveric femurs from 120 female and male donors >50 yr old: 60 without diabetes (Ctrl) and 60 with T2D for ≥10 yr. We scanned a cross-section from each diaphysis using ex vivo micro-CT (μCT), followed by cyclic reference point indentation (cRPI: 0-10 N for 20 cycles) and impact micro-indentation on the medial surface. From the medial quadrant, a tensile specimen and a single-edge notched beam (SENB) were mechanically tested to assess differences in fracture resistance. Multiple techniques characterized the organic matrix within the SENB. The cortical bone area and thickness of the diaphysis were higher in T2D than in Ctrl. The average creep indentation distance of periosteal bone tissue was significantly lower with T2D suggesting greater resistance to micro-indentation. Bone material strength index trended to be lowering in T2D than in Ctrl but only when the comparison was adjusted for age, sex, and BMI. There were also T2D-related differences in the organic matrix: (1) higher non-enzymatic and mature enzymatic crosslinks, (2) higher fluorescent advanced glycation end-products, and (3) higher thermal stability. Despite these tissue- and molecular-level differences, mechanical properties of cortical bone were similar between the 2 groups. Tensile strength was lower (p = .035), while pentosidine was higher (p = .006) in donors with CKD than donors without kidney disease, but the difference in strength (p = .055) and pentosidine (p = .151) were not strictly significant when adjusting for covariates. The elevated fracture risk in T2D may not be a problem of poor mechanical properties of cortical bone, despite alterations in the organic matrix.
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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 | 0.001 | 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) | 0.003 | 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".