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Record W137724800 · doi:10.1055/s-0030-1248043

Adverse Effects of Increased Body Mass Index and Weight on Survivorship of Total Knee Arthroplasty and Subsequent Outcomes of Revision TKA

2010· article· en· W137724800 on OpenAlexaboutno aff
Kevin Mulhall, Hassan Ghomrawi, William M. Mihalko, Quanjun Cui, Khaled J. Saleh

Bibliographic record

VenueThe Journal of Knee Surgery · 2010
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurvivorship curveBody mass indexTotal knee arthroplastyArthroplastyPhysical therapyAdverse effectSurgeryInternal medicineCancer

Abstract

fetched live from OpenAlex

To investigate the effects of increased weight and body mass index (BMI) on total knee arthroplasty (TKA) survivorship and on functional outcomes and quality of life following revision TKA, a prospective cohort study of 291 consecutive revision TKA patients was performed. Average patient BMI was 32.3 +/- 7.7, and 57% of patients were obese (BMI > or = 30). The obese group was not significantly different from the nonobese group regarding reasons for prosthesis failure; however, they were more likely to experience certain comorbidities. Body mass index and weight were both significant predictors of survivorship of primary TKA (regression coefficient BMI = -1.852, P = .004; regression coefficient weight = -0.405, P = .000) in multivariate regression. At 6-month follow-up, improvement of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) stiffness, WOMAC difficulty of function, and Knee Society Score (KSS) function scales at 6 months after revision TKA was significantly lower (at the 0.001 level, two tailed) in patients with higher BMI, weight, or both. In regression analysis, BMI was a significant predictor of Short Form-36, WOMAC difficulty of function, and KSS. Weight and BMI have deleterious effects on the longevity of primary TKA and functional and quality of life outcomes following revision TKA. These findings indicate a need for more effective management of these 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)0.002
metaresearch head score (Gemma)0.002
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 score0.044
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 integrity0.0000.001
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 head0.008
GPT teacher head0.235
Teacher spread0.226 · 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

Citations45
Published2010
Admission routes1
Has abstractyes

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