Forward lunge before and after anterior cruciate ligament reconstruction: Faster movement but unchanged knee joint biomechanics
Bibliographic record
Abstract
The forward lunge (FL) may be a promising movement to assess functional outcome after ACL reconstruction. Thus, we aimed to investigate the FL movement pattern before and after ACL reconstruction with a comparison to healthy controls to determine if differences were present. Twenty-eight ACL injured participants and 28 matched healthy controls were included. They performed FL movements while sagittal plane biomechanics of the knee and electromyography (EMG) of nine leg muscles was assessed. The ACL injured group was tested before and 10 months after surgery. The perceived knee function and activity level was assessed by questionnaires. The ACL injured group performed the FL significantly slower than the controls before surgery (mean difference: 0.41 s [95%CI: 0.04-0.79 s; p<0.05]) while they performed the FL as fast as the controls after surgery (~28% movement time reduction post-surgery). Perceived knee function and activity level improved significantly post-surgery. The knee joint flexion angle, extensor moment, power, angular velocity in the ACL injured group did not differ from pre to post-surgery. For the ACL injured group, the peak knee extensor moment observed both pre and post-surgery was significantly lower when compared to the controls. The EMG results showed minimal differences. In conclusion, at 10 months post-surgery, the FL was performed significantly faster and the movement time was comparable to that of the controls. While the perceived knee function and activity level improved post-surgery, the knee joint biomechanics were unchanged. This may reflect that knee joint function was not fully restored.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".