Surgical management of high-grade lumbar spondylolisthesis associated with Hajdu-Cheney syndrome: illustrative case
Bibliographic record
Abstract
BACKGROUND: Hajdu-Cheney syndrome (HCS) is a rare connective tissue disorder characterized by severe bone demineralization. In the spine, it is associated with the early onset of severe osteoporosis and can cause spondylolisthesis. Spinal instrumentation in the setting of severe osteoporosis is challenging because of poor resistance of vertebrae to biomechanical stress. OBSERVATIONS: A 59-year-old woman with known idiopathic HCS presented with a grade 4 L5-S1 spondylolisthesis and right L5 pedicle fracture associated with a left L5 pars fracture, causing a progressive L5 radiculopathy that was worse on the left side than the right side and bilateral foot drop. The authors performed decompressive lumbar surgery, which included a complete L5 laminectomy and resection of the left L5 pedicle. This was followed by multilevel lumbosacral instrumentation using cement-augmented fenestrated pedicle screws as well as transdiscal sacral screws and bilateral alar-iliac fixation. Postoperatively, the radicular pain resolved, and the left foot drop partially recovered. LESSONS: Stabilization of high-grade spondylolisthesis in the setting of bone demineralization disorders is challenging. The use of different instrumentation techniques is important because it increases biomechanical stability of the overall instrumentation construct.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 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".