EXTRACORTICAL BONE BRIDGING IN TUMOR ENDOPROSTHESES
Bibliographic record
Abstract
BACKGROUND: Aseptic loosening remains a major problem following prosthetic replacement after resection of periarticular tumors. Attempts to decrease the rate of loosening led to the introduction of a composite segmental prosthesis in which the shoulder of the intramedullary stem is porous-coated to allow extracortical bone bridging and bone ingrowth. The purposes of this study were to determine the extent of extracortical bone bridging around, and the amount of bone growth into, the porous-coated shoulder of endoprostheses implanted following the resection of periarticular bone tumors and to correlate the radiographic and histologic findings. METHODS: Twenty tumor endoprostheses implanted with use of the extracortical bone-bridging technique were evaluated radiographically to determine the extent of extracortical bone and the amount of bone ingrowth. Five of these endoprostheses were retrieved and subjected to histologic analysis with backscattered electron microscopy and transmitted light microscopy to determine the extent of bone ingrowth. RESULTS: At a mean of twenty-eight months postoperatively, varying amounts of extracortical bone formation were seen radiographically in all patients. Radiographs also appeared to show bone growth into the porous-coated segment of all implants. However, histologic analysis of the five retrieved prostheses revealed that none of the extracortical bone had actually grown into the porous-coated segment of the implant. CONCLUSIONS: This study confirmed that autogenous bone-grafting of the bone-implant junction of a tumor endoprosthesis consistently results in the formation of extracortical bone. Although radiographs seemed to indicate that this bone grows into the porous coating, this was not confirmed histologically. Growth of extracortical bone into the extramedullary, porous-coated portion of tumor endoprostheses in humans may not be attainable with the current prosthetic design and surgical technique.
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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 | 0.000 |
| 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".