COMPARISON OF ONE-LEG HOP FOR DISTANCE SCORES IN THE ANTERIOR CRUCIATE LIGAMENT DEFICIENT POPULATION WITH A GROUP OF HEALTHY CONTROLS
Bibliographic record
Abstract
Clinicians use performance tests, such as the one leg hop for distance (OLHD), to help assess the functional status of patients with an anterior cruciate ligament deficient (ACLD) knee. However, the validity of the OLHD as a diagnostic tool for ACLD injuries has not been properly evaluated. There are two primary sources of error associated with scoring the OLHD for this purpose: 1) variation between patients related to athletic ability; and 2) variation due to leg dominance when comparing the ACLD knee to an uninjured knee (for both within and between patient comparisons). OBJECTIVES: 1) To assess the impact of these two sources of error on the validity of the OLHD for assessing ACLD injuries; and 2) To propose methods for minimizing the impact of these errors and standardizing the reporting of patient scores. SUBJECTS: 10 males (18–42) years with an isolated ACL tear (grade 2 or 3 ligamentous laxity) and 10 gender and age-matched healthy controls. METHODS: OLHD performance was evaluated in both ACLD patients and controls. Analysis was carried out comparing the Raw scores (A), Scores corrected for athletic ability (B) and Scores corrected for athletic ability and leg dominance (C). Discriminative function analysis was used to determine the discriminative power of each of the three different analyses. RESULTS: Refer to table (•p = .012; +p = .002; *p = < .001). The mean raw score differences demonstrate only 0.18 m variation in hop distance and the ACLD hop index (0.92) is within normal limits clinically (normal hop index ± 90% in a healthy male population). Furthermore, the discriminative function analysis of the raw scores (A) only explained 30% of the hop distance and 41% of the hop index variation between ACLD and controls. However, controlling for variation due to athletic ability (B) dramatically improved the amount of variation explained between ACLD and controls (72% hop distance; 56% hop index) whereas controlling for leg dominance in addition to athletic ability (C) added less explanatory power (82% hop distance; 72% hop index). Hop distance scores demonstrated greater discriminative power than the hop index (R2 = .72–.82 vs .56–.72). CONCLUSIONS: OLHD scores must be corrected for the effects of athletic ability and leg dominance to improve their capability to discriminate between ACLD and controls. One strategy to standardize the interpretation of hop scores is to compare patient performance scores to a healthy population that takes into account athletic ability and leg dominance. Supported by O&A FoundationTable
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".