The Effect of Axial Load on the In Vivo Anterior Drawer Test of the Ankle Joint Complex
Bibliographic record
Abstract
The anterior drawer test is commonly used in the diagnosis of ankle joint mechanical instability. However, the effect of axial load on the anterior drawer test has not been examined in vivo. The purpose of the study was to assess the effect of axial load on passive anterior instability, and on the diagnostic measurement of the anterior drawer instability of the ankle joint complex. A total of 21 subjects with various degrees of ankle sprains were tested on a device that could continuously record applied anterior force and the resultant displacement of the rear-foot. Anterior drawer flexibility of the ankle joint complex in a neutral dorsi/plantar flexion position was quantified on both feet for all subjects without and with an axial load (385 N). Flexibility of the ankle joint complex in anterior drawer was defined as the slope of a linear load-displacement curve (which fitted test data with high correlation coefficients (r>0.991)). With axial load, anterior drawer flexibility was significantly reduced by 28.8% compared to that without axial load. The difference in anterior drawer flexibility between injured and intact ankles significantly decreased with axial load. An axial load increased the stability of ankle joint complex. However, axial load reduced the sensitivity of anterior drawer test to mechanical instability of the ankle joint complex.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".