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Functional outcome of staged bilateral knee replacements

2011· article· en· W2042487469 on OpenAlexaboutno aff
Ayman Gabr, Dan Withers, Jill Pope, Alasdair Santini

Bibliographic record

VenueAnnals of The Royal College of Surgeons of England · 2011
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOxford knee scoreKnee replacementTotal knee arthroplastyWOMACKnee flexionPhysical therapyKnee painRange of motionKnee JointArthroplastySurgeryOsteoarthritis

Abstract

fetched live from OpenAlex

INTRODUCTION: The aim of this study was to compare the functional outcome between the first and second knee replacement in patients undergoing staged bilateral total knee arthroplasty. METHODS: We identified 64 patients who had bilateral knee replacements and had at least one year of postoperative outcome studies. Data on pain scores, walking ability, use of walking aids, range of movement, instability, muscle strength, WOMAC® (Western Ontario and McMaster Universities) scores, SF-12 (Short Form 12) scores, American Knee Society radiological scores and length of hospital stay (LOS) were recorded. The difference in data between the first and second knee was assessed. RESULTS: Groups remained statistically comparable between the first and second operation. Four outcomes showed a significant difference between the second and first knee. The mean score for postoperative walking ability was 4.83 (second knee) vs 4.51 (first knee) (p =0.03). The mean score for postoperative walking aid requirement was 5.73 (second knee) vs 5.46 (first knee) (p=0.01). The mean postoperative SF-12 score was 54.26 (second knee) vs 52.45 (first knee) (p=0.04). The mean LOS was 4.73 days (second knee) vs 6.16 days (first knee) (p =0.05). All other data comparisons were statistically insignificant. CONCLUSIONS: Patients have a reduced LOS and continue to improve after the second procedure with regards to walking ability, use of walking aids and psychological wellbeing.

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)0.000
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 score0.050
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.082
GPT teacher head0.285
Teacher spread0.203 · 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

Citations34
Published2011
Admission routes1
Has abstractyes

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