A novel method to evaluate error in anatomical marker placement using a modified generalized Procrustes analysis
Bibliographic record
Abstract
As biomechanical research evolves, a continuing challenge is the standardization of data collection and analysis techniques. In gait analysis, placement of markers to construct an anatomical model has been identified as the single greatest source of error; however, there is currently no standardized approach to quantifying these errors. The current study applies morphometric methods, including a generalized Procrustes analysis (GPA) and a nearest neighbour comparison to quantify discrepancies in marker placement, with the goal of improving reliability in gait analysis. An extensive data-set collected by an Expert (n = 340) was used to evaluate marker placements performed by a Novice (n = 55). Variances identified through principal component analysis were used to create a modified GPA to transform anatomical data, and scaled coordinates from the Novice data-set were then scored against the Expert subset. The results showed quantitative differences in marker placement, suggesting that, although training improved consistency, systematic biases remained.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".