Removal of uncemented components: hope for the best, prepare for the worst—technical tips and tricks
Bibliographic record
Abstract
Removing well-fixed uncemented components can be challenging. With thoughtful surgical planning, appropriate surgical instruments, and proper surgical techniques, most implants can be removed expeditiously with little bone loss and minimal impact on the subsequent reconstruction. Preoperative planning is one of the most essential steps to remove uncemented implants. Obtaining previous surgical records, although tedious, should always be attempted preoperatively to determine if specific instruments will be required and to help anticipate which steps may need special attention. These include the presence of ceramic or metal bearings and the presence of acetabular screws or stem collars. Without proper preparation and available tools, the removal of implants can negatively impact the subsequent reconstruction and patient outcomes. We will describe techniques and practical tips for removing uncemented stems from the top (intramedullary) or transfemoral using an extended trochanteric osteotomy. We will also describe techniques and tools to remove uncemented acetabular shells efficiently. Case examples will highlight these clinical situations where careful planning is necessary and potential problems that may be encountered with the recurring theme of preparing for the worst but hoping for the best. We have also included cases such as removing well-fixed cementless collared stems, broken stems, and fully coated stems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".