Predicting the Longer-term Outcomes of Total Hip Replacement
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to identify the patient-level predictors (age, sex, body mass index, mental health, and comorbidity) for a sustained functional outcome at a minimum 1 year of followup after total hip replacement (THR). METHODS: We reviewed data from our registry on 636 consecutive patients from 1998 to 2005. Demographic data and the outcome scores of the Western Ontario McMaster University Osteoarthritis Index (WOMAC) and Medical Outcomes Study Short-form 36 (SF-36) scores were extracted from the database. Longitudinal regression modeling was performed to identify the predictive factors of interest. Fourteen percent of patients were missing outcomes data at 1 year of followup. RESULTS: The mean followup in our cohort was 3.3 years (range 1-6 yrs) and there were no revisions for aseptic loosening performed during this time. Mean clinical outcome scores were found to be relatively constant for the 6 years after surgery. Older age, year of followup, and greater comorbidity were identified as negative prognostic factors for a sustained functional outcome following THR (p < 0.05). CONCLUSION: Understanding of longterm surgical outcomes should be appropriately used to set realistic patient expectations of 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 | 0.008 |
| 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) | 0.001 | 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".