Severe Cortical and Trabecular Osteopenia in Secondary Hyperparathyroidism
Bibliographic record
Abstract
BACKGROUND: Peripheral quantitative computed tomography (pQCT) provides real volumetric bone density values, not only of the total, but also of trabecular and cortical bone, separately. In addition, it provides data on bone geometry that can be related to the risk of fracture. METHODS: Total, cortical, and trabecular volumetric bone mineral densities (BMD), as well as the main geometric parameters (cross-sectional area, cortical area, trabecular area, and cortical thickness) were assessed by pQCT at the distal radius in 24 hemodialysis patients affected by severe secondary hyperparathyroidism (PTH, mean +/- SD: 1444 +/- 695 pg/mL). The strength-strain index (SSI), a biomechanical parameter describing bone fragility, was also determined. RESULTS: Compared with a control group of 64 healthy age-matched subjects, volumetric BMD (mg/cm(3)) was significantly reduced in all patients (total BMD: 243 +/- 87 vs. 405 +/- 138, cortical BMD: 605 +/- 218 vs. 856 +/- 204, trabecular BMD: 95 +/- 51 vs. 182 +/- 75). Cortical area and cortical thickness showed significant modifications, while cross-sectional area did not. SSI was significantly reduced (547 +/- 125 vs. 927 +/- 306 mm(3)). PTH levels showed a significant inverse correlation with cortical BMD (r = -0.56), cortical thickness (r = -0.46), cortical area (r = -0.61), and SSI (r = -0.54). Quantitative analysis of bone demonstrated cortical porosity. CONCLUSIONS: In dialysis patients with severe secondary hyperparathyroidism, pQCT showed a significant cortical osteopenia, associated with geometric and mechanical bone impairment. Interestingly, we also found a comparable deficit of trabecular bone, which may be related to the very high PTH levels. Generalized cortical thinning, intracortical porosity and cortical-endosteal resorption ("trabecularization" of the cortical bone) are major determinants of reduced bone strength, which may be quantitated by pQCT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".