Achilles tendon allograft for augmentation of the Hanssen patellar bone grafting
Bibliographic record
Abstract
PURPOSE: Severe patellar bone loss precludes the insertion of another patellar implant in revision total knee arthroplasty (TKA) and weakens the attachment of the patella to the quadriceps tendon. Hanssen described securing a tissue flap to the patellar rim allows the delivery of cancellous bone graft into the patellar bone defect. The purpose of this study is to describe the surgical technique and early clinical results of a modified Hanssen patelloplasty with an Achilles tendon allograft. METHODS: The modified technique described in three patients undergoing revision TKA aims to augment the extensor mechanism by securing an Achilles tendon allograft to the proximal part of the patellar tendon and the distal quadriceps tendon using mattress sutures. This serves to reinforce the extensor mechanism and decrease the risk of a tear of its patellar attachment. The patellar bone defect is grafted using corticocancellous bone chips. RESULTS: Over a 12- to 48-month follow-up, all reconstructions healed with an increase in patellar bone thickness. No patient had any extensor lag, and all patients achieved 110°-125° of flexion. The Western Ontario and McMaster University knee scores improved from 53 (23-88) preoperatively to 88.5 (66-100) at last follow-up, with no reported complications. CONCLUSIONS: Augmentation of the extensor mechanism with an Achilles tendon allows for early mobilization and excellent postoperative range of motion in patients with large patella bone defects and imminent patella fracture. LEVEL OF EVIDENCE: IV.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".