Midterm survival of cementless total knee arthroplasty with a three-dimensional printed metal-backed patellar component from the American Joint Replacement Registry
Bibliographic record
Abstract
Introduction Historically, metal-backed patellar components have shown high early failure rates due to fracture, lack of osseous integration , and polyethylene wear and dissociation. We aimed to describe the survival outcomes of cementless total knee arthroplasty (TKA) utilizing the first widely used additively manufactured 3-dimensionally printed metal-backed patellar component (AM-MBP) to compare these to other cementless as well as cemented TKA cohorts. Methods There were 35,087 primary cementless TKA procedures in patients ≥65 years of age that utilized the AM-MBP component during the calendar years 2012 to 2020 that were identified from the American Joint Replacement Registry (AJRR). This AM-MBP cohort was benchmarked against age-similar (≥65 years) cohorts representing all other cementless primary TKA with a patellar component (aggregate cementless, n = 10,755) and all cemented TKA with a patellar component (aggregate cemented, n = 550,908). Cumulative percent revision curves and hazard ratios for all-cause revision were estimated using Cox proportional hazards models that adjusted for age and sex. Results The cumulative percent all-cause revision (95 % confidence interval (CI)) at 7-year follow-up was 1.9 % (1.7%, 2.1%; 1,051 at risk) for the AM-MBP and 2.5% (2.1%, 2.8%; 1,281 at risk) for other aggregated cementless. and 2.1% (2.1 %, 2.1 %; 105,641 at risk) for the aggregate cemented group. The adjusted hazard ratio for revision comparing AM-MBP with aggregate cementless was 0.77 (0.64, 0.93; P = 0.007), indicating a 23% decreased risk of revision for the AM-MBP group compared with all other cementless TKAs with a patellar component in the AJRR, controlling for age and sex. Conclusions The improved survivability of primary cementless TKA with AM-MBP versus other cementless TKA suggests that the AM-MBP construct is durable in real-world use in the Medicare-eligible population (age ≥65 years) appears to be comparable to the average durability of other cementless constructs captured by the AJRR.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".