LUMiC® endoprosthesis for pelvic reconstruction: A Canadian experience
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The LUMiC® prosthesis was introduced to reduce the mechanical complications encountered with periacetabular reconstruction after pelvic tumor resection. Few have evaluated the outcomes associated with its use. METHODS: A retrospective study from five Orthopedic Oncology Canadian centers was conducted. All patients with a LUMiC® endoprosthesis were included. Their charts were reviewed for surgical and functional outcomes. RESULTS: A total of 16 patients were followed for 28 months (3-60). A total of 12 patients (75%) had a LUMiC® after a resection of a primary sarcoma. Mean surgical time was 555 min. Four patients (25%) had a two-stages procedure. MSTS score was 60.3 preoperatively and 54.3 postoperatively. Patients got a dual mobility bearing and the silver coated implant was used in 7 patients (43.7%). Five patients (31.3%) underwent capsular reconstruction using a fabric. Silver-coating was not found to reduce infection risk (p = 0.61) and capsuloplasty did not prevent dislocation (p = 0.6). Five patients had peroperative complications (31.3%). Eight patients (50%) had an infection including all four with two-stages surgery. Dislocation occurred in five patients (31.3%) whereas no cases of aseptic loosening were reported. A total of 10 patients (62.5%) needed a reoperation. CONCLUSION: LUMiC® endoprosthesis provides low rates of aseptic loosening on medium-term follow-up. Infection and dislocation are common complications but we were unable to show benefits of capsuloplasty and silver-coated 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".