Measurement Error in Osteometric, Photographic, and Virtual Methodologies to Quantify the Torsional Profile of the Lower Limb
Bibliographic record
Abstract
OBJECTIVES: The torsional profile of the lower limb consists of femoral torsion, tibial torsion, and talar neck angle. Due to high levels of inter-individual variation and a lack of defining landmarks, these variables are difficult to precisely measure. It is important to ensure torsional profile measurement methodologies are repeatable, so that studies evaluating these variables can be compared. MATERIALS AND METHODS: Two observers collected torsional profile and linear measurements from the femur, tibia, and talus of 20 individuals using osteometric, photographic, and virtual methodologies. Intra- and interobserver error were assessed using the technical error of measurement (TEM), %TEM, and coefficient of reliability. Comparability between methods was evaluated using correlations, reduced major axis regression, and reduced mean squared error. Two methods for measuring the torsional profile were compared: a landmark method and a shape-fitting method. RESULTS: Observer error was low for linear measurements. Torsional profile measurements have higher intra- and interobserver error and lower comparability between methods than linear measurements. Shape-fitting methods for femoral torsion lowered observer error but did not improve methodological comparability. Shape-fitting methods for tibial torsion did not substantially alter observer error but improved method comparability. Shape-fitting methods for talar neck angle greatly improved method comparability, but not observer error. DISCUSSION: Linear measurements have low observer error and are highly comparable between osteometric and virtual methods. There is greater observer error and lower comparability between measurement modalities for angular measurements. Shape-fitting is a promising way to reduce observer error when measuring the torsional profile.
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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 | 0.047 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".