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Record W4390115164 · doi:10.1007/s00590-023-03798-2

Robotic-assisted versus conventional total knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials

2023· review· en· W4390115164 on OpenAlexaboutno aff
Reda Alrajeb, Mohammed Zarti, Zakaria Shuia, Osama Alzobi, Ghalib Ahmed, Aissam Elmhiregh

Bibliographic record

VenueEuropean Journal of Orthopaedic Surgery & Traumatology · 2023
Typereview
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
FundersHamad Medical Corporation
KeywordsMedicineRandomized controlled trialWOMACRange of motionRadiological weaponMeta-analysisOrthopedic surgeryCochrane LibraryPhysical therapyArthroplastyProsthesisTotal knee arthroplastySystematic reviewMEDLINESurgeryOsteoarthritisInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Robotic knee arthroplasty procedures have emerged as a new trend, garnering attention from orthopedic surgeons globally. It has been hypothesized that the use of robotics enhances the accuracy of prosthesis positioning and alignment restoration. The objective of this study was to provide a high-level, evidence-based comparison between robotic total knee replacements and conventional methods, focusing on radiological and functional outcomes. METHODS: We searched five databases from their inception until June 1, 2022, specifically targeting randomized controlled trials (RCTs) that compared the outcomes of robotic and conventional total knee replacements. We were interested in outcomes such as knee range of motion, clinical and function knee society scores, the Western Ontario and McMaster University score (WOMAC), the Hospital of Special Surgery score, complications, and radiological alignment. This review was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes guidelines. We assessed the risk of bias using the revised Cochrane risk-of-bias tool for randomized trials (RoB 2). RESULTS: Our search returned seven RCTs suitable for our analysis, which included a total of 1942 knees; 974 of these knees were implanted using robotic arms while the remaining 968 utilized jig-based knee systems. Our findings indicated that robotic knees had significantly better post-operative anatomical (OR - 0.82; 95% CI, - 1.027 to - 0.58, p value < 0.00001) and mechanical restoration (OR - 0.95; 95% CI, - 1.49 to - 0.41, p value < 0.0006). While knee range of motion (OR - 2.23; 95% CI - 4.89-0.43, p value 0.1) and femoral prosthesis position (OR - 0.98; 95% CI, - 2.03-0.08, p value 0.07) also favored robotic knees, these differences did not reach statistical significance. Both clinical and functional outcomes, as well as the rate of complications, were found to be statistically similar between the groups undergoing robotic and traditional knee replacement surgeries. CONCLUSION: This meta-analysis indicates that robotic total knee replacements offer superior post-operative anatomical and mechanical alignment compared to conventional total knee replacements. Despite this, clinical and functional outcomes, as well as complication rates, were similar between the two. These findings should be considered in light of potential confounding factors. More randomized controlled trials with the latest robotic systems are needed to confirm any superior functional and clinical outcomes from robotic-assisted surgeries. LEVEL OF EVIDENCE: I.

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)0.063
metaresearch head score (Gemma)0.071
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (broad)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.270
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0630.071
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.1330.064
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.178
GPT teacher head0.367
Teacher spread0.189 · 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; both teacher heads agree on what is shown here.

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

Citations106
Published2023
Admission routes1
Has abstractyes

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