Static vs Articulating Spacers for Two-Stage Revision Total Knee Arthroplasty: Minimum Five-Year Review
Bibliographic record
Abstract
Background The gold standard treatment for infected total knee arthroplasty (TKA) is two-stage revision. The first stage involves a temporary antibiotic spacer, which can be static or articulating; it remains unclear which is best. We aimed to compare 5-year outcomes between static and dynamic spacers. Methods One hundred and seventy-six patients with infected TKA requiring two-stage revision were enrolled. Patients were organized based on the type of spacer used during the first-stage revision. One hundred and four patients received articulating spacers, and 72 received static spacers. At 5 years, postoperative range of motion (ROM), Short Form 12 (SF-12), Knee Society Score (KSS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were recorded. Reinfection and revisions were also tracked. Results Eradication of infection was similar in both groups, with 83.7% in the articulating group and 86.1% in the nonarticulating spacer group ( P = .234). Articulating spacers resulted in significantly improved ROM (111 vs 82 degrees, P < .001), SF-12 physical component score (35.2 vs 31.0, P = .01), KSS (145.2 vs 113.7, P < .001), and WOMAC function scores (60.1 vs 51.1, P = .03) as compared to the static spacer group. Conclusions Treatment with an articulating spacer as opposed to a static spacer resulted in improved ROM, SF-12 physical component score, KSS, and WOMAC function scores at the final follow-up visit. There was no significant difference in reinfection rates. Patients requiring staged revision for infected TKA may benefit from an articulating spacer.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".