Microcracks colocalize within highly mineralized regions of cortical bone tissue
Bibliographic record
Abstract
While much work has been performed to quantify the extent of bone damage, its effects on the mechanical integrity of the tissue and its biological impact, the set of factors which gives forth to microdamage are nebulous, particularly the compositional properties local to microdamage. In this context, the current study tested the hypothesis that microcracks initiate within more mineralized regions of bone. Cortical bone specimens were taken from human male donors aged 31, 38, 53, 64, 71, and 84 years at the mid femoral diaphysis in a plane parallel to the osteonal orientation. The mineralization was assessed in a spatially resolved manner using Raman microspectroscopy. Arrays of measurements were taken over the entire area (i.e. global scans) of each sample followed by measurements in the vicinity of microcracks (i.e. local scans). Histograms of mineralization were constructed for global and local scans to determine whether the mineralization of damaged loci differed from the mean overall mineralization. Statistical analysis of this data revealed that the mean mineralization of damaged loci was significantly greater (P < 0.05) than the overall mineralization for each donor, indicating that there exists a highly-mineralized 'brittle volume' in bone. The presence of this damage prone 'brittle volume' has future implications for the assessment of fracture susceptibility.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".