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Record W4396558289 · doi:10.1186/s40798-024-00712-w

Anterior Cruciate Ligament Return to Sport after Injury Scale (ACL-RSI) Scores over Time After Anterior Cruciate Ligament Reconstruction: A Systematic Review with Meta-analysis

2024· review· en· W4396558289 on OpenAlexaff
Timothy C. Sell, Ryan Zerega, Victoria King, Charles R. Reiter, Hailey Wrona, Garrett S. Bullock, Nilani Mills, Anu M. Räisänen, Leila Ledbetter, Gary S. Collins, Joanna Kvist, Stephanie R. Filbay, Justin M. Losciale

Bibliographic record

VenueSports Medicine - Open · 2024
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsResearch CanadaUniversity of British ColumbiaArthritis Research Centre of CanadaUniversity of Calgary
Fundersnot available
KeywordsAnterior cruciate ligamentMedicineMeta-analysisAnterior cruciate ligament reconstructionACL injuryConfidence intervalPhysical therapyAthletesSystematic reviewPhysical medicine and rehabilitationSurgeryMEDLINEInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Psychological readiness is an important consideration for athletes and clinicians when making return to sport decisions following anterior cruciate ligament reconstruction (ACLR). To improve our understanding of the extent of deficits in psychological readiness, a systematic review is necessary. Objective To investigate psychological readiness (measured via the Anterior Cruciate Ligament-Return to Sport after Injury scale (ACL-RSI)) over time after ACL tear and understand if time between injury and surgery, age, and sex are associated with ACL-RSI scores. Methods Seven databases were searched from the earliest date available to March 22, 2022. Articles reporting ACL-RSI scores after ACL tear were included. Risk of bias was assessed using the ROBINS-I, RoB-2, and RoBANS tools based on the study design. Evidence certainty was assessed for each analysis. Random-effects meta-analyses pooled ACL-RSI scores, stratified by time post-injury and based on treatment approach (i.e., early ACLR, delayed ACLR, and unclear approach). Results A total of 83 studies were included in this review (78% high risk of bias). Evidence certainty was ‘weak’ or ‘limited’ for all analyses. Overall, ACL-RSI scores were higher at 3 to 6 months post-ACLR (mean = 61.5 [95% confidence interval (CI) 58.6, 64.4], I2 = 94%) compared to pre-ACLR (mean = 44.4 [95% CI 38.2, 50.7], I2 = 98%), remained relatively stable, until they reached the highest point 2 to 5 years after ACLR (mean = 70.7 [95% CI 63.0, 78.5], I2 = 98%). Meta-regression suggests shorter time from injury to surgery, male sex, and older age were associated with higher ACL-RSI scores only 3 to 6 months post-ACLR (heterogeneity explained R2 = 47.6%), and this reduced 1–2 years after ACLR (heterogeneity explained R2 = 27.0%). Conclusion Psychological readiness to return to sport appears to improve early after ACL injury, with little subsequent improvement until ≥ 2-years after ACLR. Longer time from injury to surgery, female sex and older age might be negatively related to ACL-RSI scores 12–24 months after ACLR. Due to the weak evidence quality rating and the considerable importance of psychological readiness for long-term outcomes after ACL injury, there is an urgent need for well-designed studies that maximize internal validity and identify additional prognostic factors for psychological readiness at times critical for return to sport decisions. Registration: Open Science Framework (OSF), https://osf.io/2tezs/ .

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)0.016
metaresearch head score (Gemma)0.045
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 score0.021
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.045
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0210.035
Bibliometrics0.0070.009
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.352
Teacher spread0.328 · 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

Citations32
Published2024
Admission routes1
Has abstractyes

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