Single-stage Posterolateral Vertebrectomy for the Management of Metastatic Disease of the Thoracic and Lumbar Spine
Bibliographic record
Abstract
Appropriate surgical management of spinal metastases combines maximal neural decompression with simultaneous immediate spinal column stabilization in the context of a paliative operation undertaken to improve patients' quality of life. We have used a single-stage posterolateral vertebrectomy (SPLV) for disease of the lumbar spine, combined with bilateral costotransversectomies in the thoracic spine, for these challenging cases. In this prospective cohort study of 96 consecutive patients with metastatic disease of the spinal column for we describe our surgical technique in detail, we examine our learning curve in its use and we analyze the long-term surgical and "quality of life" results in 42 patients who underwent SPLV. The mean and maximum operative blood loss was significantly lower for the SPLV group when compared with combined approaches. All patients either remained neurologically stable or had improved with surgery. Both the mean and the range visual analog scale scores were significantly improved after the SPLV. The SPLV was the only surgical approach to demonstrate a statistically significant improvement in Eastern Cooperative Oncology Group scores at 3 months after the surgery. Seventy-five percent of patients were alive at 6 months and 50% of patients survived for more than 12 months after the surgery. Eleven patients had a major complication (26%) with 9 (21%) patients required early reoperation, 7 of them for wound failure. Our data demonstrates that the SPLV represents a technically achievable improvement in surgical approach to spinal metastases when key parameters are examined. On the basis of these results, we recommend that the SPLV should be considered in all cases where resection of thoracic or lumbar spinal metastatic disease and reconstruction is contemplated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".