Anterior cervical transvertebral approach for resection of an intraspinal ventral lesion: illustrative case
Bibliographic record
Abstract
BACKGROUND: The anterior cervical corpectomy and fusion approach has been reported for the removal of ventral cervical tumors. However, the normal cervical vertebral body and the adjacent intervertebral discs have to be sacrificed. In this paper, the authors describe a novel anterior cervical transvertebral approach for the resection of cervical intraspinal ventral lesions. OBSERVATIONS: A patient presented with an anteriorly placed extramedullary cyst. An anterior cervical transvertebral open-window and close-window approach was designed and applied to resect an intraspinal ventral enterogenous cyst. With this novel technique, a square was cut through the whole vertebral body at the four sides. After the cyst resection, the bone block was restored and fixed with a titanium miniplate. The lesion was totally resected, and the compression of the spinal cord was relieved. The physiological function of the cervical spine was kept intact after the operation. There was no postsurgical complication. The cervical alignment was normal at the 1-year postoperative follow-up. LESSONS: The anterior cervical transvertebral open-window and close-window approach was developed and confirmed to be effective for the resection of cervical intraspinal lesions. The cervical physiological structure and function can be restored with this new technique.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| 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".