People Are More Variable Than Their Hop Test Would Suggest: Hop Performance and Self‐Reported Outcomes Over 11 Years Following <scp>ACL</scp> Reconstruction
Bibliographic record
Abstract
We aimed to report the trajectory of self-reported outcomes up to 11 years post-ACLR. We also explored the relationship between hop performance at 1 year and: (i) future self-reported knee outcomes; and (ii) risk of subsequent knee events. 124 participants (43 women, mean age 31 ± 8 years) were recruited at 1 year following hamstring-autograft ACLR. Hop performance was assessed with single-forward and side-hop tests. Follow-up was completed at 3 (n = 114), 5 (n = 89) and 11 years (n = 72) post-ACLR. Self-reported outcomes were assessed at each follow-up with the Knee injury Osteoarthritis Outcome Score (KOOS) pain and quality of life (QOL) subscales. Generalized linear mixed models estimated the relationship between hop performance and self-reported outcomes. Subsequent knee events (new injury/surgery) to either knee were recorded, with the relationship between hop performance and risk of subsequent knee events analyzed with Cox proportional hazards. Self-reported knee outcomes were stable (mean change < 10 points) across all timepoints but with major within-sample variability. There was a modest relationship between greater hop performance at 1 year and better future KOOS-pain (average marginal effect [AME] % improvement with + 1 cm single forward hop = 0.06% [95% CI 0.02-0.10]). A nonlinear spline relationship showed better single-forward hop performance was associated with better KOOS-QOL for scores < 108 cm, not present for higher hop scores > 108 cm. There were 21 index and 11 contralateral subsequent knee events. Hop performance was not related to risk of a subsequent knee event (hazard ratio index knee 0.99 [95% CI 0.98-1.02]). In conclusion, self-reported knee pain and quality of life were generally stable across the 11-year follow-up period. Greater hop performance at 1-year post-ACLR was related to better self-reported knee outcomes up to 11-year follow-up (of questionable clinical importance), but not associated with the risk of subsequent knee injury/surgery.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".