Distal femoral replacement with the MML system: a single center experience with an average follow-up of 86 months
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to compare the functional outcomes and complication rates after distal femoral replacement (DFR) performed with the modular Munich-Luebeck (MML) modular prosthesis (ESKA/Orthodynamics, Luebeck, Germany) in patients being treated for malignant disease or failed total knee arthroplasty. METHODS: A retrospective review of patient charts and a functional investigation (involving Musculoskeletal Tumor Society Score [MSTS], American Knee Society Score [AKSS], Oxford Knee Score [OKS], Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC], Toronto Extremity Salvage Score [TESS], the 12-Item Short-Form [SF-12] Health Survey, and a failure classification system developed by Henderson et al.) of DFR cases from 2002 to 2015 were conducted. The indications for DFR were malignant tumor resection in the femur (n = 20, group A) or failure of revision total knee arthroplasty without a history of malignant disease (n = 16, group B). RESULTS: One-hundred and twenty-nine patients were treated during the study period. Of these, 82 were analyzed for complications and implant-survival. Further, 36 patients were available for functional assessment after a mean follow-up of 86 months (range: 24-154). There were 75 complications in total. The overall failure rate for DFR was 64.6% (53/82 patients). The most common failure mechanisms were type III (mechanical failure), followed by type I (soft tissue) and type II (aseptic loosening). The mean MSTS score (out of 30) was 17 for group A and 12 for group B. All the clinical outcome scores revealed an age-dependent deterioration of function. CONCLUSION: DFR is an established procedure to restore distal femoral integrity. However, complication rates are high. Post-procedure functionality depends mainly on the patient's age at initial reconstruction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".