Evidence map›Paper›PMID 28656531›Full record

ReviewCurrent reviews in musculoskeletal medicine2017

ACL Injury Prevention: What Does Research Tell Us?

Trent Nessler, Linda Denney, Justin Sampley

Open access · bronzeAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current reviews in musculoskeletal medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 2 pooled it
6.9field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 138 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 1 country.

Trent NesslerSelect Medical, 6712 Falls Ridge Lane, College Grove, TN, 37046, USA. tnessler@selectmedical.com.
Linda DenneyNorthern Arizona University- Phoenix Biomedical Campus, 435 N. 5th St.- 6th Floor, Phoenix, AZ, 85004, USA.
Justin SampleyPinnacle Sports Therapy, 425. E. Pinnacle Peak Road, Phoenix, AZ, 85024, USA.
Lane College · USNorthern Arizona University · USPinnacle Prevention · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewMechanisms leading to anterior cruciate ligament (ACL) injury have been identified, yet re-injury or a secondary injury persists in the athletic population. The purpose of this review is to identify risk factors associated with ACL injury and investigate programs to prevent injury. RECENT

findingsFaulty mechanics during dynamic movement that cause excessive valgus force at the knee increases the risk of ACL injury. Faulty mechanics may be a result of lateral displacement of the trunk, unequal limb loading, and lack of control to avoid the valgus knee position. Altered movements that place the ACL at risk are best identified in a fatigued state; however, could be recognized in a standard dynamic assessment. The faulty movement patterns are modifiable and should be addressed in an injury prevention program. Prevention programs include various modes of exercise such as plyometrics, neuromuscular training, and strength training. This review concludes that those programs which utilize neuromuscular training and strength training at a young age show the most promise in reducing ACL injuries. An ongoing thorough dynamic examination is necessary for all athletes while adjusting the intervention program in order to decrease the risk of ACL injury.

Indexed as

Anterior cruciate ligament (ACL)Knee injuryNeuromuscular trainingPlyometricsPrevention programStrength training

Identifiers

PMID28656531
PMCPMC5577417
OpenAlexW2729852784

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.