The John Insall Award: Gender-specific Total Knee Replacement: Prospectively Collected Clinical Outcomes
Bibliographic record
Abstract
UNLABELLED: Gender-specific total knee replacement design is a recent and debated topic. We determined the survivorship and clinical outcomes of a large primary total knee arthroplasty cohort, specifically assessing any differences between gender groups. A consecutive cohort of 3817 patients with 5279 primary total knee replacements (3100 female, 2179 male) with a minimum of 2 years followup were evaluated. Preoperative, latest, and change in clinical outcome scores (WOMAC, SF-12, KSCRS) were compared. While men had higher raw scores preoperatively, women had greater improvement in all WOMAC domains including pain (29.87 versus 27.3), joint stiffness (26.78 versus 24.26), function (27.21 versus 23.09), and total scores (28.35 versus 25.09). There were no gender differences in improvements of the SF-12 physical scores. Men had greater improvement in Knee Society function (22.1 versus 18.63) and total scores (70.01 versus 65.42), but not the Knee Society knee score (47.83 versus 46.64). Revision rates were 10.2% for men and 8% for women. Women demonstrated greater implant survivorship, greater improvement in WOMAC scores, equal improvements in SF-12 scores, and less improvement in only the Knee Society function and total scores. The data refute the hypothesis of inferior clinical outcome for women following total knee arthroplasty when using standard components. LEVEL OF EVIDENCE: Level II, prognostic study. See the Guidelines for Authors for a complete description of levels of evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".