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Record W2245627008 · doi:10.4103/0366-6999.160521

Are There Any Clinical and Radiographic Differences Between Quadriceps-sparing and Mini-medial Parapatellar Approaches in Total Knee Arthroplasty After a Minimum 5 Years of Follow-up?

2015· article· en· W2245627008 on OpenAlexaboutno aff
Ai‐Bing Huang, Haijun Wang, Jia‐Kuo Yu, Bo Yang, Dong Ma, Jiying Zhang

Bibliographic record

VenueChinese Medical Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRadiographyTotal knee arthroplastyRange of motionOsteoarthritisRetrospective cohort studyArthroplastySurgeryVisual analogue scalePhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Although the early clinical outcomes of total knee arthroplasty (TKA) using minimally invasive surgery techniques have been widely described, data on the mid- to long-term outcomes are limited. We designed a retrospective study to compare the two most common TKA techniques - The modified quadriceps-sparing (m-QS) approach and the mini-medial parapatellar (MMP) approach - In terms of the clinical and radiographic parameters, over a minimum follow-up period of 5 years. METHODS: The m-QS approach was used in 31 knees and the MMP approach, in 36 knees. Knees in both groups were compared for component position and alignment, knee alignment, length of the skin incision, range of motion, Visual Analog Scale score, muscle torques, Knee Society Score, Western Ontario and McMaster Universities Osteoarthritis Index, and number of complications. RESULTS: There were no major intergroup differences in any of the clinical and radiographic outcomes assessed at the final follow-up examination. CONCLUSIONS: On the basis of numbers studied, the m-QS group, which requires more technique, showed equivalent results with the MMP group in the postoperative 5 years. Preservation of the extensor mechanism in the m-QS approach could not ensure any improvement in the clinical outcomes during the mid-term follow-up duration.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.297
Teacher spread0.242 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2015
Admission routes1
Has abstractyes

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