Countermovement Jump Kinetic Impairments in Elite Athletes Before and After ACL Injury: Force‐Time Waveform Versus Discrete Kinetic Analysis
Bibliographic record
Abstract
Pre‐injury and post‐injury countermovement jump (CMJ) force‐time data were obtained for elite athletes 6 months after anterior cruciate ligament surgery (ACLR). Jump kinetics were analysed using a traditional phase‐specific approach, and force‐time data of the CMJ waveform were analysed using statistical parametric mapping (SPM). Elite athletes ( n = 10; female n = 6, age = 22.0 ± 3.5 years, mass = 75.9 ± 11.5 kg) performed CMJ testing before (T0) and after ACLR (T1; 24 ± 3 weeks post‐surgery). Differences in discrete and continuous metrics were analysed for (1) within‐limb differences between T1 and T0 and (2) between‐limb differences at T1 and T0. Lower involved limb propulsive impulse (T1: 6.4 ± 1.6 N∙s/kg; T0: 7.7 ± 1.4 N∙s/kg, p = 0.002) and peak force (T1: 6.4 ± 1.6 N/kg; T0: 7.7 ± 1.4 N/kg, p = 0.002) were found after ACLR compared to baseline. After ACLR (T1), lower involved limb propulsive impulse was found compared to the uninvolved limb (involved: 1.26 ± 0.54 N∙s/kg; uninvolved: 1.58 ± 0.56 N∙s/kg, p = 0.007). SPM analysis revealed specific within‐limb force loss, notably reduced involved limb propulsion force at T1 compared to pre‐injury at T0 ( p < 0.001) between 92% and 99% of the CMJ (end of propulsion) and between 36% and 37% of the CMJ (i.e., late unweighting to braking phase transition). SPM analysis revealed within‐limb CMJ force loss that was not seen with the discrete analysis, highlighting the complementary value of SPM waveform analysis alongside discrete analysis to identify neuromuscular impairments in stretch‐shortening‐cycle function in elite athletes after ACLR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".