Measurement Position Influences Sex Comparisons of Distal Femoral Cartilage Thickness With Ultrasound Imaging
Bibliographic record
Abstract
The purpose was to examine (1) the effect of measurement position and sex on femoral cartilage outcomes, and (2) the association between gait biomechanics and cartilage outcomes. Fifty individuals participated (25 males and 25 females; age = 20.62 [1.80] y). Ultrasound measured femoral cartilage thickness and echo-intensity at 90°, 115°, and 140° of knee flexion. Gait outcomes included the external knee adduction and knee flexion moments. Cartilage outcomes were compared using 2 (sex) × 3 (position) repeated-measures analysis of variance. Gait and cartilage associations were assessed using stepwise regression. Medial cartilage was thicker when measured at 90° compared with 115° (P = .02) and 140° (P < .01), and 115° compared with 140°, (P < .01) in males but not in females. Cartilage was thicker at 90° compared with 140° across both sexes within all regions (P < .01). Males had thicker cartilage than females in all positions (P < .01). Echo-intensity was lower at 90° than 115° (P < .01) and 140° (P = .01) in the central and lower at 90° than at 115° (P < .01) and 140° (P = .03) in lateral regions. No association was found between gait and cartilage outcomes. Ultrasound imaging position effects cartilage features more in males compared with females. Imaging position and sex influence cartilage outcomes and should be considered in study designs and clinical evaluation.
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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 | 0.008 |
| 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) | 0.003 | 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".