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Record W4323767424 · doi:10.1177/10225536231162829

Unicompartmental knee arthroplasty versus high tibial osteotomy for medial knee osteoarthritis: A systematic review and meta-analysis

2023· review· en· W4323767424 on OpenAlexaboutno aff
Bin Zhang, Hanguang Qian, Hongfu Wu, Xiaofei Yang

Bibliographic record

VenueJournal of orthopaedic surgery · 2023
Typereview
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHigh tibial osteotomyUnicompartmental knee arthroplastyOsteoarthritisOsteotomyArthroplastyMeta-analysisSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

We aimed to systematically compare the clinical and functional outcomes between unicompartmental knee arthroplasty (UKA) and high tibial osteotomy (HTO) for the treatment of medial knee osteoarthritis (KOA). Literatures were searched from PubMed, EMBASE, the Cochrane library, Wanfang DATA, China National Knowledge Infrastructure (CNKI) and SinoMed database until December 2020. Studies comparing postoperative clinical and functional outcomes of UKA versus HTO were included. Totally, 38 studies were included, including 2368 patients with 2393 knees in HTO group and 6536 patients with 6571 knees in UKA group. There was significant difference in postoperative pain, revision rate, complications, and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score between HTO and UKA groups ( p < 0.05). No significant difference was found in excellent/good surgical results, Lysholm, Hospital for Special Surgery (HSS) score, Knee Society Knee (KSS) score, knee and function score of Knee Society (KSFS) score and Tegner score between these two groups ( p > 0.05). UKA produced less postoperative pain, less complications and superior WOMAC score, whereas HTO offered extended range of motion (ROM) and less revision rate.

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)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMeta-epidemiology (broad)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.614
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0250.018
Bibliometrics0.0020.002
Science and technology studies0.0000.000
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 head0.103
GPT teacher head0.346
Teacher spread0.244 · 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

Citations33
Published2023
Admission routes1
Has abstractyes

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