Long-term outcomes of the Nexgen© posterior stabilized knee: minimum 15 year follow—safe and effective
Bibliographic record
Abstract
PURPOSE: Studying long-term survivorship and functional outcomes for specific prostheses is critical for elucidating areas in need of design improvement. This study reports the long-term of the NexGen Posterior Stabilized (PS) Total Knee implant (TKA) (Zimmer Biomet, Warsaw IN) Performed by a single surgeon. METHODS: Data from patients treated with the NexGen PS TKA between January 2003 and December 2005 with a minimal follow-up of 15 years was collected from a prospectively collected database. Survivorship rates and Oxford Knee Scores (OKS) were obtained for those patients available for follow-up. RESULTS: Ninety-five patients met the inclusion criteria during the study period. OKS was available for 44 (46%) patients. Ten patients required revision surgery (10.52%). Implant-specific survivorship of all cases that were reviewed was 98%. Survivorship of implants in patients that we were able to reach, or deceased patients was 93%. The average Oxford Knee Score was 39.1 (14-48. SD ± 7.70) with 48 being the maximal score. CONCLUSION: Despite some concerns about durability of this implant, good longevity and function was demonstrated. At a minimum of 15 years follow-up in this cohort. Given these results design features of this system should be considered for future generations of implants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".