Complications and Risk Factors in En Bloc Resection of Spinal Tumors: A Retrospective Analysis on 298 Patients Treated in a Single Institution
Bibliographic record
Abstract
En bloc resection consists in the surgical removal of a vertebral tumor in a single piece with a sufficient margin, to improve survival and reduce recurrence rate. This procedure is technically demanding and correlates with a high complication rate. The purpose of this study is to investigate the risk factors for complications in en bloc resection and evaluate if benefits overcome the risks in term of overall survival. We retrospectively analyzed prospectively collected data of patients treated with en bloc resection between 1980 and 2021. Complications were classified according to SAVES-V2. Overall Survival was estimated using Kaplan-Meier method. A total of 149 patients out of 298 (50%) suffered from at least one complication. Moreover, 220 adverse events were collected (67 intraoperative, 82 early post-operative, 71 late post-operative), 54% of these were classified as grade 3 (in a severity scale from 1 to 6). Ten years overall survival was 67% (95% CI 59-74). The occurrence of relapses was associated to an increased risk of mortality with OR 3.4 (95% CI 2.1-5.5), while complications did not affect the overall survival. Despite a high complication rate, en bloc resection allows for a better control of disease and should be performed in selected patients by specialized surgeons.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 0.000 |
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.
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".