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Record W4393557548 · doi:10.1002/jeo2.12018

Jumping into recovery: A systematic review and meta‐analysis of discriminatory and responsive force plate parameters in individuals following anterior cruciate ligament reconstruction during countermovement and drop jumps

2024· review· en· W4393557548 on OpenAlexaff
Wasim Labban, Thaer S. Manaseer, Eric Golberg, Mark Sommerfeldt, Stephanie Nathanail, Liz Dennett, Lindsey Westover, Lauren A Beaupré

Bibliographic record

VenueJournal of Experimental Orthopaedics · 2024
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsAlberta Health ServicesUniversity of Alberta
Fundersnot available
KeywordsAnterior cruciate ligament reconstructionMeta-analysisGround reaction forcePhysical therapyConfidence intervalForce platformPhysical medicine and rehabilitationVertical jumpMedicineJumpAnterior cruciate ligamentSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Purpose Comprehensive understanding of force plate parameters distinguishing individuals postprimary anterior cruciate ligament reconstruction (ACLR) from healthy controls during countermovement jumps (CMJ) and/or drop jumps (DJ) is lacking. This review addresses this gap by identifying discriminative force plate parameters and examining changes over time in individuals post‐ACLR during CMJ and/or DJ. Methods We conducted a systematic review and meta analyses following the Preferred Reporting Items for Systematic Review and Meta‐Analyses (PRISMA) guidelines. Nine databases were searched from inception to March 2022. We included cross‐sectional papers comparing post‐ACLR with healthy controls or longitudinal studies of individuals at least 6 months postprimary ACLR while performing CMJ and/or DJ on force plates. The methodological quality was appraised using the Modified Downs and Black Checklist. Results Thirty‐three studies including 1185 (50.38%) participants post‐ACLR, and 1167 (49.62%) healthy controls, were included. Data were categorised into single‐leg CMJ, double‐leg CMJ, single‐leg DJ, and double‐leg DJ. Jump height was reduced in both single (mean difference [MD] = −3.13; p < 0.01; 95% confidence interval [CI]: [−4.12, −2.15]) and double‐leg (MD = −4.24; p < 0.01; 95% CI: [−5.14, −3.34]) CMJs amongst individuals with ACLR. Similarly, concentric impulse and eccentric/concentric impulse asymmetry could distinguish between ACLR (MD = 3.42; p < 0.01; 95% CI: [2.19, 4.64]) and non‐ACLR (MD = 5.82; p < 0.01; 95% CI: [4.80, 6.80]) individuals. In double‐leg DJs, peak vertical ground reaction forces were lower in the involved side (MD = −0.10; p = 0.03; 95% CI: [−0.18, −0.01]) but higher in the uninvolved side (MD = 0.15; p < 0.01; 95% CI: [0.10, 0.20]) when compared to controls and demonstrated significant changes between 6 months and 3 years post‐ACLR. Conclusion This study identified discriminative kinetic parameters when comparing individuals with and without ACLR and also monitored neuromuscular function post‐ACLR. Due to heterogeneity, a combination of parameters may be required to better identify functional deficits post‐ACLR. Level of Evidence Level III.

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)0.010
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.035
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0150.028
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.353
Teacher spread0.325 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations14
Published2024
Admission routes1
Has abstractyes

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