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Record W3028770256 · doi:10.14740/jmc3474

Percutaneous Endoscopic Surgery for Lumbar Discal Cyst: Two Case Reports

2020· article· en· W3028770256 on OpenAlexvenueno aff
Shiqi Suo, Yanan Chen, Xirui Mao, Song Chen, Zhi‐An Fu

Bibliographic record

VenueJournal of Medical Cases · 2020
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePercutaneousSurgeryLumbarDecompressionMagnetic resonance imagingCystBack painLow back painRadiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.060
GPT teacher head0.354
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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