Treatment of femoral bone loss in revision total hip arthroplasty: a clinical practice review
Bibliographic record
Abstract
Patient and implant selection is essential to optimize outcome. Femoral bone loss classifications such as the American Academy of Orthopaedic Surgeons, Gross, and Paprosky classifications permit surgeons to systematically manage bone stock deficiencies and guide implant selection. Here we provide a comprehensive report on the pitfalls and management of this reconstructive challenge. Preoperative planning remains vital to the treatment of femoral bone loss in revision hip arthroplasty and the authors believe it is essential and should include the entire femur. This commonly includes imaging for bone loss such as Judet views or computed tomography scan and must include the entire femur though additional radiographs such as Judet views apply more for acetabular bone loss as opposed to femoral bone loss. All patients should have pre-operative work up to exclude infection. If any of these results area elevated, an aspirate and sampling is required to guide microbiological management. Classically with regards femoral revision surgery, uncemented fixation has proven to give the best outcomes but surgeons must remain flexible and use cemented fixation when necessary. Adequate proximal bone stock permits the use of implants used in primary joint surgery. Implants with proximal modularity can be used in cases where bone stock allows for superb proximal bone support. The vast majority of femoral revisions have inadequate proximal bone stock, thus distally fixed stems should be used and have been shown to provide both axial and rotational stability provided there is an intact isthmus. Taper fluted stems can provide good outcomes even in cases of major bone loss. However, with severe bony loss, impaction grating or the use of a megaprotsthesis is sometimes necessary and is down to surgeon choice and preference. This article has been written as a guide for management and summarises the best evidence available.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".