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Record W2782515668 · doi:10.5792/ksrr.17.006

High Tibial Osteotomy versus Unicompartmental Knee Arthroplasty for Medial Compartment Arthrosis with Kissing Lesions in Relatively Young Patients

2018· article· en· W2782515668 on OpenAlexaboutno aff
Seung Min Ryu, Jae Woo Park, Ho Dong Na, Oog‐Jin Shon

Bibliographic record

VenueKnee Surgery and Related Research · 2018
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsUnicompartmental knee arthroplastyMedicineHigh tibial osteotomyCompartment (ship)SurgeryArthroplastyOsteotomyOsteoarthritisGeology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study is to compare the clinical and radiographic outcomes of high tibial osteotomy (HTO) and unicompartmental arthroplasty (UKA) in advanced medial compartment arthritis accompanied by kissing lesions in relatively young patients. MATERIALS AND METHODS: Forty-five patients were divided into the HTO (n=23) and UKA (n=22) groups. Clinically, we evaluated the Lysholm knee scoring scale, visual analogue scale, Hospital for Special Surgery, and Western Ontario and McMaster Universities Osteoarthritis index scores preoperatively, 6 and 12 months postoperatively, and at the final follow-up. Radiographically, we measured the femoral-tibial angle and mechanical axis deviation preoperatively and at the final follow-up. RESULTS: All clinical outcomes gradually improved in both groups from the postoperative period to the final follow-up. At the final follow-up, all clinical outcomes were slightly better in the UKA group than in the HTO group; however, differences were not statistically significant. CONCLUSIONS: HTO is comparable to UKA in terms of clinical outcomes. Thus, the results of this study suggest that HTO might be a good alternative treatment to UKA for medial unicompartmental arthritis accompanied by kissing lesions in relatively young patients.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.331
Teacher spread0.272 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations52
Published2018
Admission routes1
Has abstractyes

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