No Difference in Gender-specific Hip Replacement Outcomes
Bibliographic record
Abstract
UNLABELLED: Gender-specific total hip arthroplasty (THA) design has been recently debated with manufacturers launching gender-based designs. The purpose of this study was to investigate the survivorship and clinical outcomes of a large primary THA cohort specifically assessing differences between genders in clinical outcomes, implant survivorship, revisions as well as sizing and offset differences. We reviewed 3461 consecutive patients receiving 4114 primary THAs (1924 women, 1537 men) between 1980 and 2004 with a minimum of 2 years followup (mean, 11.33 +/- 6.5 years). A subset of patients with complete implant data was reviewed for sizing and offset differences. Preoperative, latest, and change in clinical outcome scores as well as Kaplan-Meier analysis were performed. Men had higher raw clinical outcome scores preoperatively and postoperatively. Differences in change of clinical outcome scores were found only in the WOMAC pain score in favor of the female cohort (39.4 versus 36.1). Survivorship and revision rate were not significantly different. Men used larger stems with greater stem lengths, neck offset, and neck lengths. Current implant systems were sufficiently versatile to address the different size and offset needs of male and female patients. These data suggest there is no apparent need for a gender-designed THA system. LEVEL OF EVIDENCE: Level II, prognostic study. See the Guidelines for Authors for a complete description of levels of evidence.
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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) | 0.000 | 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) | 0.009 | 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".