Reintroduction of Running After Anterior Cruciate Ligament Reconstruction With a Hamstrings Graft: Can We Predict Short-Term Success?
Bibliographic record
Abstract
CONTEXT: Return to running (RTR) after anterior cruciate ligament reconstruction (ACLR) is a crucial milestone. However, how and when to start a running program are uncertain. OBJECTIVE: To explore the feasibility of a structured program to reintroduce running after ACLR and evaluate the predictive value of potential predictors of short-term success. DESIGN: Longitudinal cohort study. SETTING: Local research center and participants' homes. PATIENTS OR OTHER PARTICIPANTS: Thirty-five participants were recruited after ACLR. INTERVENTION(S): Program with a progression algorithm to reintroduce running (10 running sessions in 14 days). MAIN OUTCOME MEASURE(S): The criterion for short-term success was no exacerbation of symptoms. Potential predictors were (1) the International Knee Documentation Committee (IKDC) subjective knee form score, (2) ACL Return to Sport after Injury questionnaire score, (3) quadriceps and hamstrings strength, (4) step-down endurance test, and (5) modified Star Excursion Balance test. Descriptive statistics were performed to study the feasibility of the RTR program, and Poisson regression analysis was used to evaluate predictors of success. RESULTS: Of the 34 participants, 33 completed the RTR program. Sixteen participants experienced some temporary exacerbation of symptoms, but only 1 had to stop the program. The initial IKDC score was the only significant predictor of a successful RTR, with an area under the receiver operating characteristic curve of 80.4%. An IKDC cut-off of 63.7/100 differentiated responders and nonresponders with the highest sensitivity and specificity (77.8% and 75.0%, respectively). A participant with an IKDC score above this threshold had a 3-fold greater chance of success. CONCLUSIONS: Our results confirm the feasibility of our RTR program and progression algorithm after ACLR. Clinicians should use an IKDC score of >64 as a criterion to reintroduce running after ACLR to increase the likelihood of short-term success.
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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 | 0.003 | 0.015 |
| 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) | 0.002 | 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".