A New Method for Whole Bone Analysis of Bilateral Asymmetry
Bibliographic record
Abstract
Postcranial asymmetry of bones has been mostly studied with linear or angular measurements. Although conclusive, these measurements fail to capture the differences of the entire bone surface. Here, we develop a methodology to measure whole bone bilateral asymmetry from 3D models. We demonstrate the method using the humerus and the second metacarpal. We compare right and left bones of the same individual (bilateral variation) to that of different scans of the same bone (interscan variation) and of the same bone from different individuals (interindividual variation) to show that the method functions and is able to segregate different degrees of variation. The interscan variation is the lowest, while the interindividual variation is the greatest, and the bilateral variation falls between the other two. Visual comparisons, using color maps, illustrate on the bone where the asymmetry is most marked. As expected, the interscan comparisons show very little variation in shape, while the interindividual comparisons reveal extensive variation. In bilateral comparisons, some patterns were observed. In the humerus, the radial groove, the deltoid tuberosity, and the olecranon fossa were usually the most asymmetrical regions. The epiphyses are also more asymmetrical than the diaphysis. For the MC2, the attachments for the palmar interossei muscles and the articular facets with the MC3 were the most asymmetrical regions. These results demonstrate that this new method helps identify areas of asymmetry that would otherwise be difficult to observe.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".