Hip rotation muscle strength is implicated in the progression of early post-traumatic osteoarthritis: A longitudinal evaluation up to 5 years following ACL reconstruction
Bibliographic record
Abstract
INTRODUCTION: Following ACL reconstruction (ACLR), deficits in hip muscle strength and relationships to future outcomes are unknown. METHODS: 111 participants one year after ACLR, completed hip external rotation (ER) and internal rotation (IR) strength assessment. At 1 (n = 111) and 5 (n = 74) years post-ACLR, participants completed a battery of functional, symptomatic (Knee Osteoarthritis Outcome Score (KOOS)) and structural assessments (radiography, magnetic resonance imaging (MRI)). Cartilage health of the patellofemoral and tibiofemoral compartments was assessed with the semiquantitative MRI Osteoarthritis Knee Score. Hip rotation strength was compared between-limbs, and relationships between hip strength at 1 year and functional, symptomatic and cartilage outcomes at 1 and 5 years were investigated with regression models. RESULTS: The index (ACLR) limb had weaker hip ER (but not IR) strength compared to the contralateral side (standardised mean difference ER = -0.33 (95%CI -0.60, -0.07; IR = -0.11 (95%CI -0.37, 0.15). Greater hip ER and IR strength was associated with superior function at 1 and 5 years, and better KOOS-Patellofemoral symptoms at 5 years. Greater hip ER strength was associated with lower odds of worsening tibiofemoral cartilage lesions at 5 years (odds ratio 0.01, 95%CI 0.00, 0.41). CONCLUSION: Hip rotation strength may play a role in worsening function, symptoms and cartilage health after ACLR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".