The push-through total femoral prosthesis for revision of a total hip or knee replacement with extreme bone loss
Bibliographic record
Abstract
PURPOSE: The aim of the present study was to assess outcomes of using the push-through total femoral prothesis (PTTF) for revision total hip replacement with extreme bone loss. METHODS: 10 consecutive patients who received PTTF between 2012 and 2018 for revision hip arthroplasty were included in the study. Primary functional outcomes were assessed using Harris Hip Score (HHS), Toronto Extremity Salvage Score (TESS) and Musculoskeletal Tumor Society (MSTS) scores. Range of motion, complications, and ambulatory status were also recorded to assess secondary outcomes. RESULTS: 2 of 10 patients underwent surgery with PTTF for both knee and hip arthroplasty revision. The mean time between index surgery and PTTF was 15 years (3-32 yrs). Acetabular components were revised in 6 of 10 patients during PTTF application. After a mean follow-up of 5.9 years, hip dislocations occurred in 3 patients. All of the dislocated hips were ones with retained conventional non-constrained acetabular bearings. Patient satisfaction was high (MSTS: 67%, HHS: 61.2%, TESS 64.6%) despite high re-operation rate (40%) and minor postoperative problems. CONCLUSIONS: PTTF should be considered for hip and knee arthroplasty revision procedures in patients with an extreme bone defect. Consistent usage of constrained liners should be considered to avoid hip dislocation, which was our main problem following the procedure.
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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 | 0.002 |
| 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) | 0.001 | 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".