111 Using biomechanics to assess the countermovement jump as a tool to measure male and female adolescents with ACL injury
Bibliographic record
Abstract
Introduction Adolescent anterior cruciate ligament (ACL) injuries have increased substantially over the last two decades and some 25% will experience a re-injury following surgery, with injury rates highest among females. As such, improved return to activity metrics are imperative. Vertical jump performance is a one commonly used tool, however performance standards and the role of the injured or non-injured limb in achieving jump height is unknown for adolescent males and females. As such, the purpose of this study was to (1) assess performance in ACL injured and uninjured adolescents, and (2) assess limb contributions to this performance. Materials and Methods Thirty-one ACL injured and thirty-eight control female adolescents, and fifteen ACL injured and twenty-five control male adolescents performed a countermovement jump (CMJ) task while whole body 3D kinematics were recorded. Maximum jump height and the maximum sagittal hip, knee, and ankle velocities were calculated. Females and males were analysed separately, while contrasts were made between limbs and injury status. Results Jump height was 13% lower in the ACLi compared to CON, while the ACLi contralateral limb also produced greater hip, knee and ankle angular velocities compared to their injured limb in females. No difference was found in jump height between ACLi and CON, however the contralateral limb of the ACLi males had greater hip and knee extension angular velocities. Neither male nor female controls had inter-limb differences. Conclusion ACLi adolescents shielded the injured limb to achieve similar jump performance. This leads to asymmetrical joint loading and may explain injury risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.003 | 0.001 |
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".