Prevention of Secondary Injury after Anterior Cruciate Ligament Reconstruction: Relationship between Pelvic-Drop and Dynamic Knee Valgus
Bibliographic record
Abstract
(1) Background: Optimal neuromuscular, Lumbo-Pelvic-Hip Complex, and lower extremity control are associated with decreased risk factors for secondary anterior cruciate ligament (ACL) injury. This study aimed to analyze any asymmetries and malalignments in the Lumbo-Pelvic-Hip Complex and the lower limbs at 6 months after ACL reconstruction (ACLR). (2) Methods: We conducted an exploratory retrospective observational single-center study in patients during the outpatient postoperative rehabilitation program at ICOT (Latina, Italy). From January 2014 to June 2020, 181 patients were recruited, but only 100 patients (86 male 28 ± 0.6 years, 178 ± 0.5 height; 14 female 24 ± 2.0 years, 178 ± 3.0 height) were eligible for the inclusion criteria and studied 6 months after ACL reconstruction surgery. (3) Statistical analysis: Student’s t-tests and Pearson’s product-moment correlation coefficient were used to determine significant differences between affected and non-affected limbs and variables’ association. (4) Results: The study shows a decrease in neuromuscular control of the Lumbo-Pelvic-Hip Complex and dynamic adaptive valgus of the knee at 6 months after ACLR (mean difference between pathological and healthy limb of dynamic adaptive valgus was −10.11 ± 8.19° 95% CI −14.84 to −9.34; mean value was 16.3 ± 6.8° 95% CI 14.04 to 18.55 for healthy limb and 4.2 ± 3.1° 95% CI 3.15 to 5.21 for pathological limb, p < 0.0001). The results also showed a relationship between dynamic adaptive valgus and contralateral pelvic drop (r = 0.78, 95% CI 0.62 to 0.88, magnitude of correlation very large). (5) Conclusions: The analysis showed an associative correlation between decreased postural control of the pelvic girdle and dynamic adaptive valgus of the knee in 38% of patients; this study highlights the usefulness of the Single-Leg Squat Test (SLST) as a clinical/functional assessment to evaluate the rehabilitation process and as a preventive tool to reduce the risk of second ACL injuries during the return to sport.
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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 | 0.001 | 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) | 0.002 | 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".