Frequency and reason for reoperation following non-invasive expandable endoprostheses: A systematic review
Bibliographic record
Abstract
BACKGROUND: Non-invasive expandable endoprostheses (NIEPR) utilize an external electromagnetic field to drive an innate mechanical gearbox. This lengthens the extremity following oncological resections in children with a predicted limb length discrepancy (LLD), facilitating limb-salvage. This review was conducted to assess NIEPR implant survival rates and identify modes of implant failure unique to these prostheses. METHODS: Medline, EMBASE and the Cochrane Library databases were searched for all manuscripts evaluating implant survival of NIEPRs implanted into skeletally immature patients following resection of extremity sarcomas. Minimum follow-up of 12 months or implant failure was required for inclusion. Failures were classified using the latest ISOLS classification and exact implant-specific failure modality was also identified. RESULTS: 19 studies met inclusion criteria. Mean age was 10.0 years (7.7 - 11.4 years). The most common locations for NIEPR implantation were the distal femur (343, 76.7%) and proximal tibia (53, 119%). Mean follow-up was 65.3 months (19.4 - 163 months). The overall implant revision rate was 46.2% (0 - 100%); implant specific revisions included maximal prosthesis lengthening with persistent LLD (10.4%), failed extension mechanism (6.1%), implant fracture (7.7%), hinge fracture (1.4%) and bushing wear (0.9%). Persistent clinically significant (>20 mm) LLD at final follow-up was present in 19.2% (0 - 50%) of patients. The mean MSTS score was 85.1% (66.7-96.3%) at final follow-up. CONCLUSION: Implant-related failures are the most common reason for NIEPR revision. Implant reliability appears to be improved with current designs. A sub-classification to the current classification system based on implant-specific failures for NIEPRs is proposed.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".