Contractile rate of force development after anterior cruciate ligament reconstruction—a comprehensive review and meta‐analysis
Bibliographic record
Abstract
STUDY DESIGN: Comprehensive review and meta-analysis. BACKGROUND: The recovery in rapid force production measured as the rate of force development (RFD) is not clear after anterior cruciate ligament reconstruction (ACLR). OBJECTIVES: To evaluate (a) time-course change of between-limb asymmetries in isometric knee extension/flexion RFD in individuals post-ACLR and (b) differences in RFD between individuals post-ACLR and healthy controls. METHODS: A literature search of Web of Science, SPORTDiscus, PubMed-MEDLINE, and ScienceDirect identified 10 eligible studies (n = 246) assessing RFD after ACLR. RESULTS: Standard mean difference (SMD) for early-phase (<100 ms) knee extensor RFD was -1.07 (95% CI: -1.46, -0.68) when comparing ACLR vs uninjured limb, while SMD for late-phase (≥100 ms) RFD was -0.85 (95 CI%: -1.27, -0.42). SMD for early- and late-phase knee flexor RFD was -0.74 (95% CI: -1.19, -0.29) and -0.79 (95% CI: -1.19, -0.39), respectively. Comparing ACLR limbs to uninjured controls, knee extensor SMD for early- and late-phase RFD was -1.42 (95% CI: -2.10, -0.73) and 1.09 (95% CI: -1.81, -0.38). For the knee flexors, SMD for early- and late-phase RFD was -0.78 (95% CI: -1.96, -0.39) and -1.14 (95% CI: -1.60, -0.67). CONCLUSIONS: Anterior cruciate ligament reconstruction limbs demonstrated sustained post-surgical suppression in RFD capacity for the knee extensors/flexors compared to the contralateral limb as well as to healthy controls. Monitoring of RFD should be considered throughout rehabilitation and return to sport (RTS) after ACLR to assess the effectiveness of post-operative rehabilitation. Post-surgical ACLR rehabilitation should include training interventions to enhance RFD.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".