Delayed percutaneous intradiscal hydrogel herniation causing neurological injury after minor trauma: illustrative case
Bibliographic record
Abstract
BACKGROUND: Percutaneous intradiscal hydrogel injection has been used to treat low-back pain (LBP) due to degenerative disc disease with or without mild radicular pain. Complications from these procedures are underreported. In this case lesson, the authors present a rare case of a patient with herniated intradiscal hydrogel following a minor trauma leading to neurological injury. OBSERVATIONS: A 36-year-old female who had been previously treated with L4-5 and L5-S1 intradiscal hydrogel injections for LBP presented with painful, progressive right-sided lower-extremity weakness after experiencing a twisting trauma. Cross-sectional imaging results revealed a herniated hydrogel-based disc implant in the spinal canal. She underwent an urgent L4-5 laminectomy for decompression and removal of the herniated implant. A large annular defect was noted. Postoperatively, she had resolution of her right leg pain but experienced persistent right foot drop requiring an ankle-foot orthosis. LESSONS: Herniated intradiscal hydrogel implants can lead to permanent neurological injury. While the risk factors for hydrogel herniation have not been elucidated, they can be related to pre-existing disruptions to the annulus and/or posterior longitudinal ligament, excess volume of hydrogel injection, and insufficient fixation time. Further research is needed to determine the safety and efficacy of this technology. https://thejns.org/doi/10.3171/CASE24394.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| 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".