Postoperative Pain Is Associated With Psychological and Physical Readiness to Return to Sports One‐Year After Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
Purpose To identify whether any patient factors, injury factors, or symptom severity scores are associated with either psychological or physical readiness to return to sport after anterior cruciate ligament reconstruction (ACLR). Methods Consecutive patients with an ACL injury that required surgical treatment were included in this study. All patients completed the single‐legged hop testing and the Anterior Cruciate Ligament Return to Sport Index (ACL‐RSI) at 1 year postoperatively. Multivariable regression analysis models were used to determine whether an independent relationship existed between baseline patient factors (age, sex, BMI, preinjury Marx Activity Score), injury factors (meniscal tear and chondral injury), physical symptoms (Knee Injury and Osteoarthritis Outcome Score [KOOS] for pain and symptoms), and the dependent variables of physical and psychological readiness to return to sport (single‐legged hop and ACL‐RSI). Results Of the 113 patients who were included, 37% were female, and the mean age of our population was 28.2 years (SD = 8.1). Multivariable regression models demonstrated that patient‐reported pain symptoms at 1 year postoperatively, as measured by the KOOS pain subscale, was significantly associated with both ACL‐RSI score (Beta estimate: 1.11 [95% CI: .62‐1.60] P < .001) and the ability to pass the single‐legged hop test (OR: 1.07 [95% CI: 1.004‐1.142] P = .037). Conclusions Patients with higher reported pain levels at 1 year following ACLR have lower psychological and physical readiness to return to sport. Level of Evidence Level 3, retrospective cohort study.
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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 | 0.004 |
| 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 | 0.000 | 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".