TOTAL HIP ARTHROPLASTY: WHY DO PRIMARIES FAIL?
Bibliographic record
Abstract
1069 primary hip arthroplasty (THA) (416 males) and 1846 revision (798 males) patients were matched for sex, age and date of primary THA. Data were collected via retrospective chart review. Time to revision averaged 9.5 years. Revision THA patients were younger at primary THA (55 vs. 64 years), had a higher body mass index (27 vs. 30) and more frequently had a cemented acetabulum (p To determine whether patient (age, gender, underlying disease, body mass index), surgical (surgical approach), and prosthetic (cemented vs. uncemented acetabular or femoral component, femoral head size) factors predict time to revision arthroplasty of primary total hip arthroplasty (THA). Patients who are younger when undergoing primary THA, have secondary osteoarthritis (OA) or dysplasia, are obese, and have a cemented acetabulum with a small femoral head by a posterior approach are at increased risk for revision THA. This study identified important, potentially modifiable patient, surgical and prosthetic factors that are adverse predictors of outcome. For the period 1980 to 2000, 1069 primary hip arthroplasty patients (416 males) and 1846 revision arthroplasty (798 males) patients were matched for sex, age and date of primary THA within two years. Revision THAs for infection were excluded. Data were collected via retrospective chart review. Time to revision THA averaged 9.5 years. In univariate analysis, patients who had revision THA were younger at primary THA (55 vs. 64 years, p Funding: This work was supported in part by a grant from the Canadian Orthopaedic Foundation and The Arthritis Society
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".