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Record W4312222240 · doi:10.1016/j.arthro.2022.11.039

Lack of Efficacy of Bone Void Filling Materials in Medial Opening‐Wedge High Tibial Osteotomy: A Systematic Review and Network Meta‐analysis

2022· review· en· W4312222240 on OpenAlexaboutno aff
Yunhe Mao, Mingke You, Lei Zhang, Jian Li, Weili Fu

Bibliographic record

VenueArthroscopy The Journal of Arthroscopic and Related Surgery · 2022
Typereview
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisRandomized controlled trialCochrane LibraryHigh tibial osteotomySystematic reviewSurgeryMEDLINEOsteoarthritisInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To systematically review the clinical and radiologic outcomes of isolated medial opening-wedge high tibial osteotomies with different bone void filling materials and to compare the outcomes by network meta-analysis. METHODS: This systematic review and network meta-analysis included searches of Medline, Embase, Cochrane Library, Web of Science, and Scopus from inception to July 30, 2022, for clinical comparative studies comparing 2 or more bone void filling materials in patients undergoing medial opening-wedge high tibial osteotomies. We performed Bayesian random-effect network meta-analyses to summarize the evidence and applied the Grading of Recommendations Assessment, Development, and Evaluation frameworks to rate the certainty of evidence, calculate the absolute effects, and present the findings. Cochrane Risk of Bias Tool 2.0 and modified Newcastle-Ottawa Scale were used to assess the risk of bias. RESULTS: In total, 2,755 citations were identified by our search, of which 25 eligible trials, including 10 randomized controlled trials and 15 nonrandomized comparative trials (NCTs) enrolled 1,420 participants and 6 different interventions (autografts, allografts, synthetic grafts, mixed grafts, xenografts, and without grafts). There were some concerns on the risk of bias assessment among randomized controlled trials, and the median Newcastle-Ottawa Scale score was 6 for NCTs. All fillers showed no significantly superior treatment effects when compared with unfilled group in final Knee Society Scoring, Western Ontario and McMasters Universities score, time to bone union (TBU), and loss of correction (LOC). Exceptionally, moderate-certainty evidence suggested that autograft would produce superior incidence of complete bone union (CBU) than the unfilled at postoperative 1 year (odds ratio [OR] 13.0, 95% confidence interval [CI] 1.60-95.6), whereas low- to very low-certainty evidence suggested allografts (OR 0.2, 95% CI 0.06-0.52) and synthetic grafts (OR 0.29, 95% CI 0.10-0.68) would result in inferior CBU. Low-certainty evidence suggested allografts would result in larger LOC angle than unfilled group (mean difference 1.1, 95% CI 0.1-2.3). As for TBU, low-certainty evidence suggested mixed grafts would take longer time to reach clinical bone union (mean difference -14.04, 95% CI -21.0 to -6.9). CONCLUSIONS: There is a lack of efficacy for different bone void filling materials to result better outcomes in Knee Society Scoring, Western Ontario and McMasters Universities score, TBU, and LOC than without graft. Although applying the autografts would produce a superior possibility of radiologic CBU than other fillers, because of the inclusion of NCTs, the overall certainty of the evidence synthesis is low. LEVEL OF EVIDENCE: Level Ⅲ, meta-analysis of Level I randomized controlled trials and Level Ⅱ-Ⅲ non-randomized comparative trials.

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)0.028
metaresearch head score (Gemma)0.067
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: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.028
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.067
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.038
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.336
Teacher spread0.268 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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