PREDICTORS OF QUALITY OF LIFE OUTCOMES AFTER REVISION TOTAL HIP REPLACEMENT.
Bibliographic record
Abstract
Introduction: The aims of this study were to determine predictors of pain, function and activity level 1–2 years after revision hip arthroplasty and define quality of life outcomes after revision total hip replacement. Methods: A prospective cohort of 222 patients who underwent revision hip arthroplasty were evaluated. Predictive models were developed and proportional odds regression analyses were performed to identify factors that predict quality of life outcomes at 1 and 2 years post surgery. The dependent outcome variables were WOMAC function, pain and UCLA activity. The independent variables included patient demographic, surgery specific and objective parameters including baseline Western Ontario McMaster Universities (WOMAC) osteoarthritis index, and the Short Form-12 mental component. The Loess method was used to plot the change of WOMAC and SF-12 scores over time. Results: There was a significant improvement (p When considering WOMAC pain as an outcome variable, factors predictive of improving category outcome included baseline WOMAC function (p= 0.001), age between 60–70 (p Conclusions: Predictors of quality of life outcomes after revision hip replacement-showed that although some patient specific and surgical specific variables were important, age, gender, Charnley class and baseline WOMAC function had the most robust associations with outcomes.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".