Method for accurate removal of trabecular bone samples from a curved articulating surface of the distal femur
Bibliographic record
Abstract
BACKGROUND: Knowing the mechanical properties of trabecular bone is critical for many branches of orthopaedic research. Trabecular bone is anisotropic and the principal trabecular direction is usually aligned with the load it transmits. It is therefore critical that the mechanical properties are measured as close as possible to this direction, which is often perpendicular to a curved articulating surface. METHODS: This study presents a method to extract trabecular bone cores perpendicular to a curved articulating surface of the distal femur. Cutting guides were generated from computed tomography scans of 12 human distal femora and a series of cutting tools were used to release cylindrical bone cores from the femora. The bone cores were then measured to identify the angle between the bone core axis and the principal trabecular axis. FINDINGS: The method yielded an 83% success rate in core extraction over 10 core locations per distal femur specimen. In the condyles, 97% of extracted cores were aligned with the principal trabecular direction. INTERPRETATION: This method is a reliable way of extracting trabecular bone specimens perpendicular to a curved articular surface and could be useful across the field of orthopaedic research.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".