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Record W2761876677 · doi:10.1177/0363546517732541

Return to Sport-Specific Performance After Primary Anterior Cruciate Ligament Reconstruction: A Systematic Review

2017· review· en· W2761876677 on OpenAlexaff
Nicholas G. Mohtadi, Denise Chan

Bibliographic record

VenueThe American Journal of Sports Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAnterior cruciate ligament reconstructionMedicineMEDLINEData extractionSystematic reviewCritical appraisalPhysical therapyAthletesMeta-analysisEvidence-based medicineAnterior cruciate ligamentPhysical medicine and rehabilitationAlternative medicineSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Physicians counseling athletes on the prognosis of sport-specific performance outcomes after anterior cruciate ligament reconstruction (ACLR) depend on the published literature. However, critical appraisal of the validity and biases in these studies is required to understand how ACLR affects an athlete's ability to return to sport, the athlete's sport-specific performance, and his or her ability to achieve preinjury levels of performance. PURPOSE: This review identifies the published prognostic studies evaluating sport-specific performance outcomes after ACLR. A risk of bias assessment and summaries of return to sport and career longevity results are provided for each included study. STUDY DESIGN: Systematic review. METHODS: Electronic databases (Ovid MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and PUBMED) were searched via a defined search strategy with no limits, to identify relevant studies for inclusion in the review. A priori defined eligibility criteria included studies measuring sport-specific performance within an athlete's sport, before and after primary ACLR. Reference lists of eligible studies were hand-searched for additional relevant studies. Data extraction was performed by use of a standardized spreadsheet. Each included study was assessed by use of 6 bias domains of the Quality in Prognosis Studies tool to critically appraise study participation, study attrition, prognostic factors, outcome measurement, confounders, and statistical analysis and reporting. Two authors independently performed each stage of the review and reached consensus through discussion. RESULTS: Fifteen pertinent prognostic studies evaluated sport-specific performance outcomes and/or return to play after ACLR for athletes participating in competitive soccer, football, ice hockey, basketball, Alpine ski, X-Games ski and snowboarding, and baseball. Twelve of these studies were considered to have a high level of bias. CONCLUSION: This review demonstrated that most high-performance or professional athletes returned to their preinjury level of sport after ACLR. The bulk of evidence suggests there was a measurable decrease in performance statistics, although this is highly sport-specific. A paucity of literature is available that addresses sport-specific performance in athletes after ACLR. This review has determined that the available literature is highly biased and must be read with caution. CLINICAL RELEVANCE: By better understanding the validity and biases in the published literature, physicians can provide more informed prognoses about return to sport-specific performance after ACLR while considering risk factors relevant to their patients. REGISTRATION: CRD42016046709 (International Prospective Register of Systematic Reviews, https://www.crd.york.ac.uk/prospero/ ).

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)0.004
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0120.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.331
Teacher spread0.303 · 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 designSystematic review
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

Citations87
Published2017
Admission routes1
Has abstractyes

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