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Record W2467894214 · doi:10.2298/vsp150901154g

Total knee arthroplasty in patients with rheumatoid arthritis-midterm results

2016· article· en· W2467894214 on OpenAlexaboutno aff
Z. Blagojevic, Boris Gluščević, Boban Djordjevic

Bibliographic record

VenueVojnosanitetski pregled · 2016
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRheumatoid arthritisInterquartile rangeWOMACTotal knee arthroplastyArthroplastyArthritisSurgeryOsteoarthritisKnee JointKnee arthritisRadiographyInternal medicine

Abstract

fetched live from OpenAlex

Background/Aim. Total knee arthroplasty in patients with rheumatoid arthritis is an effective method of eliminating pain and improving functional status, but it is associated with a number of unique challenges. The aim of this study was to evaluate the clinical and radiographic outcomes of total knee arthroplasty, as well as patient satisfaction, in this series of patients with rheumatoid arthritis. Methods. Between January 2001 and 2012, 108 total knee arthroplasties in 78 patients with rheumatoid arthritis were performed, utilizing a posteriorstabilized prostheses. The average age of the patients was 58 ? 12.4 years, and 88% were females. Median follow-up of patients was 80 months with interquartile range of 34 months (min-max: 36-132 months). Results. Average Knee Society score improved from preoperative 18 ? 11.4 to postoperative 83 ? 3.5, and Functional Knee Society score from 21 ? 9.9 to 50 ? 5.9. Western Ontario and McMaster Universities Arthritis (WOMAC) knee injury and osteopaedic outcome scores improved from 23 ? 6.4 to 69 ? 4.6 postoperatively. In 25 (23.14%) knees radiolucent lines of less than 2 mm were found and they were not progressive. Survival rate excluding deep infection was 99.1%. Conclusion. Total knee arthroplasty is an effective procedure of treatment of damaged knee joint in patients with rheumatoid arthritis.

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)0.000
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 score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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