Outcomes of Stemmed versus Un-Stemmed Varus-Valgus Constrained Components in Primary Total Knee Arthroplasty
Bibliographic record
Abstract
PURPOSE: The necessity of stemmed components when performing a varus-valgus constrained (VVC) primary total knee arthroplasty (TKA) is unclear. The purpose of this study is to compare the outcomes of primary VVC TKA with and without stems at a minimum of two years. METHODS: Patients in our prospectively collected database with primary VVC TKAs were identified. Patient demographics, prosthesis data, time in vivo, characteristics of revision, and radiographs and PROMs were compared between the stemmed and un-stemmed cohorts. RESULTS: Sixty-five patients with 69 primary VVC TKAs were identified; 17 were implanted with stems and 52 without stems. Five of the stemmed TKAs (5/17) required revision at 15.1 years, while only one of the un-stemmed TKA (1/52) required a revision at 21.6 years (p=0.003) for aseptic loosening. Of the 5 stemmed TKAs requiring revision, 3 were for aseptic loosening and 2 were for PPJI. The un-stemmed cohort had a significantly higher final total KSS (p=0.048). CONCLUSION: There was no increase in aseptic loosening or revision surgery in patients with non-stemmed primary VVC TKA compared to those with stemmed VVC TKA at mid-term follow-up. Utilizing non-stemmed TKA with VVC in appropriate cases is safe and may reduce cost, shorten operative time, and preserve bone-stock.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".