Hop Testing Provides a Reliable and Valid Outcome Measure During Rehabilitation After Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although various hop tests have been proposed as performance-based outcome measures following anterior cruciate ligament (ACL) reconstruction, limited reports of their measurement properties exist. The purpose of this study was to investigate the reliability and longitudinal validity of data obtained from hop tests during rehabilitation after ACL reconstruction. SUBJECTS: Forty-two patients, 15 to 45 years of age, who had undergone ACL reconstruction participated in the study. METHODS AND MEASURES: The study design was prospective and observational with repeated measures. The subjects performed a series of 4 hop tests on 3 separate occasions within the 16th week following surgery and on a fourth occasion 6 weeks later. The tests were a single hop for distance, a 6-m timed hop, a triple hop for distance, and crossover hops for distance. Performance on the ACL-reconstructed limb was expressed as a percentage of the performance on the nonoperative limb, termed the "limb symmetry index." Subjects also completed the Lower Extremity Functional Scale and a global rating of change questionnaire. RESULTS: Intraclass correlation coefficients for limb symmetry index values ranged from .82 to .93. Standard errors of measurement were 3.04% to 5.59%. Minimal detectable changes, at the 90% confidence level, were 7.05% to 12.96%. Changes in hop test scores on the operative limb were statistically greater than changes on the nonoperative limb. Pearson correlations (r) between change in hop performances and self-reported measures ranged from .26 to .58. DISCUSSION AND CONCLUSION: The results show that the described series of hop tests provide a reliable and valid performance-based outcome measure for patients undergoing rehabilitation following ACL reconstruction. These findings support the use and facilitate the interpretation of hop tests for research and clinical practice.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".