Retrograde Application of Humerus Fassier-Duval Rod in Osteogenesis Imperfecta: A New Surgical Technique
Bibliographic record
Abstract
BACKGROUND: The aim of this study is to describe the technique of retrograde application of Fassier-Duval (FD) rod for the humerus in patients with osteogenesis imperfecta (OI). This technique was developed to overcome the downsides of the previously used techniques of humerus rodding. METHODS: The study was done at a tertiary care pediatric orthopaedic hospital from April 2014 to August 2021. Skeletally immature patients with OI who underwent retrograde FD rodding were included. This surgery was performed for humeral shaft fractures/bowing limited to the distal half of the bone to ensure appropriate stability of the fixation. Surgical technique of the procedure is described in detail. RESULTS: Six patients with OI, of which 2 (33.3%) had FD rodding bilaterally, were included. The mean age at rodding was 7.6±3.5 (range: 3 to 14) years. The mean duration of postoperative follow-up was 45.5±18.0 (range: 24 to 75) months. All patients had full healing of the fracture/osteotomy, with functional alignment of their humeri. No surgical complications were observed; however, 1 (12.5%) segment only had a traumatic humerus fracture following a fall that was associated with rod migration, occurring 60 months postoperatively. This was treated with a retrograde FD rodding again, with fracture augmentation with plate and screws. CONCLUSIONS: The retrograde FD rodding technique of the humerus in OI patients is relatively simple and preserves the soft tissue surrounding the shoulder joint, with favorable outcomes. Studies with larger sample size and long-term follow-up duration are needed. LEVEL OF EVIDENCE: Level IV-case series.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".