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Record W4415926715 · doi:10.1002/ksa.70177

Clinical effects of different remnant‐preserving versus standard nonpreserving techniques in anterior cruciate ligament reconstruction: A systematic review and meta‐analysis

2025· review· en· W4415926715 on OpenAlexaboutno aff
Xuanbo Liu, Xiaoke Li, Dijun Li, Zijuan Fan, Zilu Ge, Jingwei Jiu, Ruoqi Li, Zaikai Zhuang, Songyan Li, Guangyuan Du, Ligan Jia, Yijia Ren, Jiao Jiao Li, Lei Yan, Bin Wang

Bibliographic record

VenueKnee Surgery Sports Traumatology Arthroscopy · 2025
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of ChinaRebecca L. Cooper Medical Research Foundation
KeywordsAnterior cruciate ligamentOrthopedic surgeryAnterior Cruciate Ligament InjuriesAnterior cruciate ligament reconstructionMEDLINEOrthopedic Procedures

Abstract

fetched live from OpenAlex

PURPOSE: In anterior cruciate ligament reconstruction (ACLR), the clinical benefits and limitations of nonpreserving versus different remnant-preserving ACLR are not well defined. There is also a lack of systematic analysis of ACL remnant length. METHODS: Database searches were performed in PubMed, Embase, Web of Science and Cochrane CENTRAL from inception to 25 April 2025. Risk of bias was assessed using the Cochrane Collaboration's tool and the Newcastle-Ottawa scale (NOS) critical appraisal tools. Certainty of evidence was evaluated using the grading of recommendations assessment, development and evaluation (GRADE) framework. The reported outcomes used for analysis included stability-related indicators, functional scores, synovial coverage and complications. Subgroup analyses were performed across three remnant-preserving surgical approaches: Augmentation, tension and sparing. Meta-regression and network meta-analyses were performed to investigate whether improved outcomes were associated with remnant length. Sensitivity analysis was used to assess the robustness of the results. RESULTS: Our analysis included 36 articles. For stability, the remnant-preserving (R) group showed improved outcomes compared to the nonremnant (NR) group in the Lachman test (weighted mean difference [WMD] = 1.61, p = 0.0003), Side-to-side anterior laxity (SSD, WMD = -0.27, p = 0.03) and Pivot shift test (WMD = 1.34, p = 0.03), with the highest improvement observed in the Sparing subgroup. For functional scoring, the R group exhibited significantly higher Lysholm knee scoring scale scores (WMD = 1.52, p = 0.01), Tegner athletic ability evaluation score (WMD = 0.40, p < 0.00001), and International Knee Documentation Committee (IKDC) subjective scores (WMD = 1.00, p = 0.001), with the Tension subgroup showing the highest overall improvement. The R group also achieved better synovial coverage (odds ratio [OR] = 2.00, p = 0.0004) and lower failure (OR = 0.48, p = 0.002), particularly in the augmentation subgroup. Meta-regression indicated a correlation between increased graft length and reduced SSD (β = -1.33, p = 0.01), improved IKDC scores (β = 20.03, p = 0.01), and fewer complications (β = -2.61, p = 0.01). Network meta-analysis indicated a 75% remnant preservation ratio minimised SSD alongside enhanced functional scores. CONCLUSION: Remnant-preserving ACLR showed improvements in stability and synovial coverage. However, in terms of clinical outcomes, the improvement in functional recovery has not reached a significant level. Sparing exceled in improving stability, while augmentation effectively reduced failure and tension favoured function. Increasing remnant preservation length significantly improved stability and function concomitantly reducing complications. CLINICAL TRIALS: CRD42024550746. 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)0.014
metaresearch head score (Gemma)0.029
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 score0.023
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.029
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0230.043
Bibliometrics0.0060.007
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 head0.037
GPT teacher head0.375
Teacher spread0.339 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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