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Record W2158519548 · doi:10.2522/ptj.20050329

Is there evidence that proprioception or balance training can prevent anterior cruciate ligament (ACL) injuries in athletes without previous ACL injury?

2006· review· en· W2158519548 on OpenAlexaff
Jessica L Owen, Sean T. Campbell, Sara J Falkner, Christine Bialkowski, Alex T Ward

Bibliographic record

VenuePhysical Therapy · 2006
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProprioceptionAnterior cruciate ligamentAthletesACL injuryBalance (ability)MedicinePhysical medicine and rehabilitationAnterior Cruciate Ligament InjuriesPhysical therapySurgery

Abstract

fetched live from OpenAlex

The purpose of “Evidence in Practice” is to illustrate how evidence is gathered and used to guide clinical decision making. This article is not a case report. The examination, evaluation, and intervention sections are purposely abbreviated. A collegiate-level soccer player was instructed by her coach to incorporate a proprioceptive component into her training program. He suggested that she purchase a balance board and immediately begin a program that he designed. She approached her physical therapist (SJF) for more information. I immediately recognized that, because of her sex and sport of choice, she would be at high risk for an anterior cruciate ligament (ACL) injury. Hewett et al1 estimated that as many as 2,200 ACL ruptures per year occur in female collegiate athletes in both the recreational and competitive ranks. Treatment and rehabilitation costs are estimated at $17,000 per ACL injury, which do not take into account the potential loss of long-term participation, loss of scholarship funding, and future disability from arthritic changes in a reconstructed knee.1 For these reasons, a shift toward injury prevention is warranted. Injury prevention for the ACL can take many forms, including a variety of training protocols, athlete education, and bracing. Current studies focus on neuromuscular training as a preventive measure, with programs that include strength, flexibility, plyometrics, sport-specific agility drills, speed enhancement, balance, and athlete education.1–7 A clinician who understands the individual components of these programs could optimize injury prevention and aid athletes in appropriate program design and equipment purchases. In the case of this athlete, my colleagues and I focused on the use of proprioception or balance training and its effect on incidence of ACL injury. We searched the literature to answer our clinical question “Is there evidence that proprioception or balance training can prevent ACL injuries in athletes …

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.096
GPT teacher head0.400
Teacher spread0.304 · 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 teacher head, not a consensus.

Study designOther design
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

Citations20
Published2006
Admission routes1
Has abstractyes

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