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Record W4200153114 · doi:10.1177/23259671211058105

Vertical Drop Jump Biomechanics of Patients With a 3- to 10-Year History of Youth Sport–Related Anterior Cruciate Ligament Reconstruction

2021· article· en· W4200153114 on OpenAlexaff
Gregor Kuntze, Alberto Nettel‐Aguirre, K. Lorenzen, Jessica Küpper, Janet L. Ronsky, Jackie L. Whittaker, Carolyn A. Emery

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsResearch CanadaUniversity of British ColumbiaAlberta Children's HospitalAlberta Bone and Joint Health InstituteUniversity of Calgary
Fundersnot available
KeywordsAnterior cruciate ligamentMedicineBiomechanicsVertical jumpJumpAnterior Cruciate Ligament InjuriesOrthodonticsSports medicineReturn to sportKnee flexionPhysical medicine and rehabilitationPhysical therapyAnatomyAthletes

Abstract

fetched live from OpenAlex

Background: A better understanding of movement biomechanics after anterior cruciate ligament reconstruction (ACLR) could inform injury prevention, knee injury rehabilitation, and osteoarthritis prevention strategies. Purpose: To investigate differences in vertical drop jump (VDJ) biomechanics between patients with a 3- to 10-year history of youth sport–related ACLR and uninjured peers of a similar age, sex, and sport. Study Design: Cross-sectional study. Level of evidence III. Methods: Lower limb kinematics and bilateral ground-reaction forces (GRFs) were recorded for participants performing 10 VDJs. Joint angles and GRF data were analyzed, and statistical analysis was performed using 2 multivariate models. Dependent variables included sagittal (ankle, knee, and hip) and coronal (knee and hip) angles at initial contact and maximum knee flexion, the rate of change of coronal knee angles (35%-90% of the support phase; ie, slopes of linear regression lines), and vertical and mediolateral GRFs (normalized to body weight [BW]). Fixed effects included group, sex, and time since injury. Participant clusters, defined by sex and sport, were considered as random effects. Results: Participants included 48 patients with a history of ACLR and 48 uninjured age-, sex-, and sport-matched controls (median age, 22 years [range, 18-26 years]; 67% female). Patients with ACLR demonstrated steeper negative coronal knee angle slopes (β = –0.04 deg/% [95% CI, –0.07 to –0.00 deg/%]; P = .025). A longer time since injury was associated with reduced knee flexion (β = –0.2° [95% CI, –0.3° to –0.0°]; P = .014) and hip flexion (β = –0.1° [95% CI, –0.2° to –0.0°]; P = .018). Regardless of ACLR history, women displayed greater knee valgus at initial contact (β = 2.1° [95% CI, 0.4° to 3.8°]; P = .017), greater coronal knee angle slopes (β = 0.05 deg/% [95% CI, 0.02 to 0.09 deg/%]; P = .004), and larger vertical GRFs (landing: β = –0.34 BW [95% CI, –0.61 to –0.07 BW]; P = .014) (pushoff: β = –0.20 BW [95% CI, –0.32 to –0.08 BW]; P = .001). Conclusion: Women and patients with a 3- to 10-year history of ACLR demonstrated VDJ biomechanics that may be associated with knee motion control challenges. Clinical Relevance: It is important to consider knee motion control during activities such as VDJs when developing injury prevention and rehabilitation interventions aimed at improving joint health after youth sport–related ACLR.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.007
GPT teacher head0.220
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2021
Admission routes1
Has abstractyes

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