Tips and Tricks for Installation of the SLIM Nail in Osteogenesis Imperfecta with Narrow Medullary Canals: A Surgical Guide with Case Insights
Bibliographic record
Abstract
INTRODUCTION: Osteogenesis imperfecta (OI) presents significant surgical challenges due to bone fragility, narrow medullary canals, and complex deformities. While telescoping rods like the Fassier-Duval (FD) system are commonly used in growing patients, they are unsuitable when the canal diameter is too small or when patients approach skeletal maturity. The Simple Locking Intramedullary (SLIM) nail offers a solid, non-telescoping alternative in these cases. METHODS: We describe the surgical technique for SLIM nail implantation and highlight key technical pearls developed through institutional experience, focusing on preoperative planning, intraoperative strategies, and the management of unique anatomical challenges in OI patients. RESULTS: Three cases illustrate the application of these techniques: the first case demonstrates SLIM nail insertion in a 3-year-old child with a narrow IM canal to correct significant bowing; reaming was performed retrograde from the osteotomy site for the proximal segment and antegrade for the distal segment. The second case is a 15-year-old OI patient with a disengaged FD rod and narrow IM canal showing insertion of SLIM rod, and the third case is a 16-year-old patient with femoral deformity and telescoping rod who needed revision with SLIM nail and supplemental plate fixation. CONCLUSIONS: The SLIM nail is a viable option for select OI patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".