Endoprosthetic replacements for bone tumors: review of the most recent literature
Bibliographic record
Abstract
Purpose of review: To evaluate results following endoprosthetic reconstruction, management of complications, and new concepts that are emerging. Recent findings: Despite improvements, there are still many complications that put salvaged limbs at a high risk. Infection has become the most common of these, and its incidence has not decreased over the years. It can sometimes be managed successfully, but requires aggressive therapy and good quality soft tissue coverage. A new way to fix implant to bone has shown interesting short-term results. Expandable implants offer an appealing alternative for children, and noninvasive lengthening can be achieved without the burden linked to repeated surgery. Revision procedures, unfortunately, are part of the treatment plan. Reports on uncemented implants have been very scarce in this review, and the issue of ‘cemented or cementless’ remains unresolved. Authors reported very low aseptic loosening rates following cementation of tight-fit stems. Reverse shoulder designs need to be explored as a possible means to improving active shoulder motion and function following proximal humeral resection. Rotationplasty is recognized, even in adults, as an effective means of salvaging endoprosthetic complication. Summary: Information published in 2006–07 will impact the way we select implant and reconstruction procedures, as well as the way we prevent and manage complications.
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) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".