Percutaneous Endoscopic Surgery for Lumbar Discal Cyst: Two Case Reports
Bibliographic record
Abstract
Discal cyst is a rare disease, the pathogenesis is not yet clear and its symptoms are very similar to lumbar disc herniation. Although some cases may regress spontaneously, most cases of lumbar discal cysts are treated surgically. At present, there is no consensus on the treatment of this disease. The authors report the clinical usefulness of the percutaneous endoscopic transforaminal surgery technique in two patients with the lumbar 4-5 discal cyst. The clinical symptoms of both patients were unilateral lower extremity pain and lower back pain. Magnetic resonance imaging of the lumbar spine revealed lumbar discal cysts, causing compression to the spinal dura and roots. Both patients received conservative treatment for more than 6 months, but the clinical symptoms persisted so surgical treatment by percutaneous endoscopic transforaminal surgery without additional discectomy was performed under local anesthesia. The symptoms were relieved immediately after removal of the discal cysts. Postoperative magnetic resonance imaging showed that both patients had complete excision of discal cysts and complete decompression of the treated segmental. There were no recurrent lesions and complications during the follow-up period. We believe that percutaneous transforaminal endoscopic surgery could be a safe, mini-invasive and appropriate method for the treatment of discal cysts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.015 | 0.005 |
| 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".