The Effect of Exercise on Anterior-Posterior Translation of the Normal Knee and Knees with Deficient or Reconstructed Anterior Cruciate Ligaments
Bibliographic record
Abstract
Exercise may result in increased laxity in the knee. Anterior translation in 40 normal knees, 33 consecutive anterior cruciate ligament-deficient knees, and 30 randomly chosen anterior cruciate ligament-reconstructed knees was measured using the KT-1000 arthrometer before and after the participants ran for 15 minutes on a neutral-incline treadmill. A single observer blinded to the status of each knee tested all participants. There was a significant increase in anterior translation in the normal (mean, 0.75 mm), anterior cruciate ligament-deficient (mean, 0.62 mm), and anterior cruciate ligament-reconstructed knees (mean, 0.25 mm) after exercise. In addition, the amount of anterior translation after exercise was significantly different when these groups were compared with each other. Post hoc analysis using Tukey's procedure indicated that anterior translation in the anterior cruciate ligament-reconstructed knee was significantly less than in the normal and anterior cruciate ligament-deficient knees. Therefore, repetitive loading exercise contributes to an increase in anterior translation in normal, anterior cruciate ligament-deficient, and anterior cruciate ligament-reconstructed knees, and the anterior cruciate ligament-reconstructed knee does not respond to repetitive loading in the same manner as a normal knee.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".