Predicting Short-Term Outcome of Metal-on-Metal Hip Resurfacing (MOMHR)
Bibliographic record
Abstract
Introduction: The aim of this study was to research factors affecting the short-term outcome of metal-on-metal hip resurfacing (MOMHR) and develop a multivariate regression model that may predict outcome. Materials and Methods: This was a prospective study of 154 patients who underwent MOMHR and were followed for a minimum of 1 year. Fourteen independent variables (age, gender, diagnosis, co-morbidities, body mass index (BMI), pr-operative Western Ontario and McMaster Universities Osteoarthritis (WOMAC) physical component/stiffness (S)/pain (P), short form 12 (SF-12) physical (SP), SF-12 mental (SM), acetabular and femoral component sizes, operative time, and estimated blood loss) were analyzed using correlation and multivariate regression analyses. Multivariate regression model was tested by using an independent cohort for validation. Results: Correlation analyses found four variables that significantly influence short term MOMHR outcome. These include comorbidities (C, P = 0.0001), preoperative SF-12 mental (SM, P = 0.0004), BMI ( P = 0.0006), and gender (G, P = 0.0454). By multivariate analysis, the subsequent regression model was obtained with an R 2 value of 0.3816: Outcome = G*4.72 [FIGURE DASH] BMI*0.70 [FIGURE DASH] C*0.11 + SM*0.31 + 87.44. The average predicted outcome using this equation did not differ significantly from the observed WOMAC physical function outcome at a minimum of 1 year postoperatively. Conclusion: To the best of our knowledge, this study is the first reported multivariate analysis of factors affecting MOMHR and confirms the correlation of some of the previously proposed factors such as gender, BMI, comorbidities, and preoperative function. The multivariate regression equation can be used to predict the short-term outcome of MOMHR.
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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 | 0.000 |
| 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".