Technique of Reverse Smith Petersen Osteotomy (RSPO) in a Patient With Fixed Lumbar Hyperlordosis and Negative Sagittal Imbalance
Bibliographic record
Abstract
In Brief Study Design. Case report. Objective. To determine the viability and safety of Reverse Smith Petersen Osteotomy (RSPO) to re-established sagittal balance in patients with fixed lumbar hyperlordosis. Summary and Background Data. Lumbar hyperlordosis is seen as a compensatory mechanism in thoracic Scheuermann disease and in sagittal decompensation in severe neuromuscular scoliosis. Hyperlordosis may also be seen after overcorrection with spinal osteotomies, but rarely causes clinically significant negative sagittal imbalance because of the thoracic compensation. We describe a case using a kyphosing osteotomy to treat hyperlordosis in a patient that was treated with a pedicle subtraction osteotomy for post-Harrington kyphosis. Methods. The radiographs and clinical chart were reviewed of a patient treated with a RSPO at L2–L3 to correct the negative sagittal imbalance created by a previous extension of her fusion to the sacrum with a pedicle subtraction osteotomy. Results. A reduction in the lumbar lordosis by 20° at L2–L3 and restoration of the global sagittal balance was achieved with the RSPO. Conclusion. RSPO is a viable surgical technique that can be used to re-establish sagittal balance in patients with fixed lumbar hyperlordosis. Appreciation of a patient's balanced sagittal alignment and available compensatory mechanisms can help ensure appropriate osteotomies are performed. Iatrogenic hyperlordosis with fixed negative sagittal imbalance is a rare clinical entity. Hyperlordosis in the presence of a rigid thoracic spine may result in a protuberant lower rib cage and prominent buttocks. This technique describes the correction of hyperlordosis with a Reverse Smith Petersen Osteotomy of the lumbar spine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".