Metabolic Syndrome and the Functional Outcomes of Hip and Knee Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: Patients with an elevated systemic inflammatory state are known to report greater pain with knee osteoarthritis (OA). We investigated the influence of risk factors of metabolic syndrome (MetS) on patient function before and after hip and knee replacement surgery. METHODS: A total of 677 consecutive patients with primary knee replacement and 547 consecutive patients with primary hip replacement with at least one MetS risk factor were reviewed from our joint registry. Demographic variables of age, sex, and comorbidity were retrieved. MetS risk factors were defined as body mass index (BMI) > 30 kg/m(2), diabetes, hypertension, and hypercholesterolemia. Baseline and 1-year Western Ontario McMaster University Osteoarthritis Index (WOMAC) scores were compared across patients by number of MetS risk factors, ranging from 1 to 4. Linear regression modeling was used to evaluate the effects of the MetS risk groups and the individual metabolic abnormalities on predicting baseline and 1-year WOMAC scores. Knee and hip patients were reviewed separately. RESULTS: The knee and hip patients showed a significant difference in sex distribution, BMI, and mean comorbidity across risk groups (p < 0.05). Unadjusted analysis showed that baseline and 1-year WOMAC scores, for both knee and hip patients, increased significantly with increasing number of MetS risk factors (p < 0.05). The linear regression model with the individual metabolic abnormalities was found to be more predictive of outcome than one with the number of MetS risk factors. Hypertension and obesity were the metabolic factors most predictive of a poorer outcome following hip surgery as compared to just obesity for knee patients. CONCLUSION: Patient function following joint replacement surgery, particularly hip surgery, is negatively affected by metabolic abnormalities perhaps secondary to the systemic proinflammatory state. This knowledge should be used when counseling patients prior to surgery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".