Statistical prediction of bone microstructure degradation to study patient dependency in osteoporosis
Bibliographic record
Abstract
Numerical prediction of osteoporosis evolution is a challenging objective in medicine, particularly when one desires to account for patient dependency. The use of statistical methods to reconstruct bone microstructure distribution could be a helpful tool for this prediction, as they are able to provide different types of microstructures that can be optimized to fit with each patient. An initial bone sample was obtained from high-resolution X-ray computed tomography (HRμCT). Its microstructure evolution in time using a previously developed degradation model was used as the ground truth. Statistical bone microstructures were reconstructed at different stages of this evolution using two-point correlation functions (TPCFs). A blind search approach is used to find the optimized statistical microstructures, and the optimized coefficient showed less than 2% TPCF error between the statistical reconstruction and the degraded model. The statistical models also showed less than 13% error in the corresponding mechanical properties. The results showed a good correlation between the developed approach and the ground truth. The method could be extrapolated to account for the physical characterization of patient dependency to predict bone density loss over time.
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 | 0.001 | 0.004 |
| 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".