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Record W2946284983 · doi:10.1177/2309499019848079

A prospective randomized study comparing navigation versus conventional total knee arthroplasty

2019· article· en· W2946284983 on OpenAlexaboutno aff
Rajkumar Selvanayagam, Vijay Kumar, Rajesh Malhotra, Deep Narayan Srivastava, Vijay Kumar Digge

Bibliographic record

VenueJournal of orthopaedic surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTotal knee arthroplastyArthroplastyRandomized controlled trialProspective cohort studySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Navigation is associated with improved accuracy in alignment. However, its influence on clinical outcome is inconclusive. The aim of this study was to compare the component alignment and functional outcome in patients undergoing navigation-assisted and conventional total knee replacement (TKR). MATERIALS AND METHOD: A prospective randomized study consisting of two groups (group A and group B) was carried out. Group A consisted of patients undergoing TKR using conventional jig-based method, whereas group B consisted of patients undergoing TKR using computer navigation-assisted method. We measured and compared the coronal and sagittal plane alignment in X-ray and rotational alignment in computed tomography scan between both groups. Functional outcome was analysed using Knee Society Score (KSS) and Western Ontario and McMaster University scale (WOMAC) score. RESULTS: A total of 50 patients were randomized into two groups A and B each with 25 patients. Navigation was associated with more accuracy in mechanical axis alignment ( p = 0.011) and femoral component rotation ( p = 0.033). The mean follow-up was 4.6 years (range 48-62 months). There was no statistically significant difference between the groups with respect to KSS and WOMAC score at the minimum follow-up of 4 years. CONCLUSION: We concluded that even though navigation-assisted system is associated with better accuracy, there was no difference in clinical outcome at an average follow-up of 4.6 years.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.163
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.275
Teacher spread0.255 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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