Demonstration of a novel method to explore osteon tilt in the human femoral cortex
Bibliographic record
Abstract
Osteon tilt, defined as the combination of an osteon's vertical angle and horizontal orientation, remains poorly characterized due to technical limitations in large-scale 3D bone microarchitecture analysis. This study developed a methodology combining traditional serial sectioning using circularly polarized light microscopy with Geographic Information Systems (GIS) to visualize and quantify osteon tilt across the entire femoral cortex. Spatial variation of osteon tilt was analyzed using 1219 osteons across 8 octants and 3 circumferential rings. Vertical osteon angle varied regionally, with acute angles in the anterolateral region and obtuse angles in the posterior region. Osteons demonstrated a general posterior inclination with opposite orientations on the medial and lateral cortices. Vertical angle positively correlated with osteon volume, with the strongest correlation found in the anterior and lateral regions. A paired sample t-test showed no significant difference between serial sections, confirming the preservation of sectional alignment. Osteon tilt patterns may reflect the femur's complex loading environment: smaller, acutely angled osteons predominate in tension-bearing regions, while larger, obtusely angled osteons occur in compression-bearing regions. This GIS-based method enables a quantitative, comprehensive assessment of osteon morphology and provides insights into bone's adaptive remodeling response to biomechanical forces.
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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 | 0.000 |
| 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".