Which Patients Are Most Likely to Benefit From Total Joint Arthroplasty?
Bibliographic record
Abstract
OBJECTIVE: To evaluate patient predictors of good outcome following total joint arthroplasty (TJA). METHODS: A population cohort with hip/knee arthritis (osteoarthritis [OA] or inflammatory arthritis) ages ≥55 years was recruited between 1996 and 1998 (baseline) and assessed annually for demographics, troublesome joints, health status, and overall hip/knee arthritis severity using the Western Ontario and McMaster Universities OA Index (WOMAC). Survey data were linked with administrative databases to identify primary TJAs. Good outcome was defined as an improvement in WOMAC summary score greater than or equal to the minimal important difference (MID; 0.5 SD of the mean change). Logistic regression and Akaike's information criterion were used to determine the optimal number of predictors and the best model of that size. Log Poisson regression was used to determine the relative risk (RR) for a good outcome. RESULTS: Primary TJA was performed in 202 patients (mean age 71.0 years; 79.7% female; 82.7% with >1 troublesome hip/knee; 65.8% knee replacements). Mean improvement in WOMAC summary score was 10.2 points (SD 18.05; MID 9 points). Of these patients, 53.5% experienced a good outcome. Four predictors were optimal. The best 4-variable model included pre-TJA WOMAC, comorbidity, number of troublesome hips/knees, and arthritis type (C statistic 0.80). The probability of a good outcome was greater with worse (higher) pre-TJA WOMAC summary scores (adjusted RR 1.32 per 10-point increase; P < 0.0001), fewer troublesome hips/knees (adjusted RR 0.82 per joint; P = 0.002), OA (adjusted RR for rheumatoid arthritis versus OA 0.33; P = 0.009), and fewer comorbidities (adjusted RR per condition 0.88; P = 0.01). CONCLUSION: In an OA cohort with a high prevalence of multiple troublesome joints and comorbidity, only half achieved a good TJA outcome, defined as improved pain and disability. A more comprehensive assessment of the benefits and risks of TJA is warranted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".