Surgical Technique for Revision of the Distally Migrated Fassier–Duval Femoral Rod in Osteogenesis Imperfecta: A Case Report
Bibliographic record
Abstract
Background/Objectives: Managing long bone fractures and deformities in osteogenesis imperfecta (OI) with telescoping rods is a common but challenging procedure. A rare complication is the distal migration of the rod’s proximal female component, which complicates standard revision surgery. This article aims to describe a surgical technique for the revision of a distally migrated Fassier–Duval (FD) femoral rod. Methods: We present the case of an 8-year-old girl with OI type IV who experienced distal migration of her right femoral FD rod—the surgical technique involved extracting the rod retrogradely through the fracture/osteotomy site. We used a trephine to remove surrounding bone within the canal, thereby preserving the critical bone stock in the greater trochanter needed for secure fixation of the revision implant. Results: The distally migrated female component was successfully removed through the trephined canal with a combination of axial traction and rotational force. The proximal bone stock was preserved, allowing for the stable placement of a revision FD rod. Conclusions: The retrograde trephine technique is a viable and effective strategy for revising a distally migrated telescoping rod in patients with OI. This approach prioritizes the preservation of proximal bone stock, which is crucial for the stability and longevity of the revision implant.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| 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".