Pre-operative Spine Tumor Embolization: Clinical Outcomes and Effect of Embolization Completeness
Bibliographic record
Abstract
Abstract Background and Purpose To assess the association between the impact of the completeness of pre-operative spine tumour embolization and clinical outcomes including estimated blood loss (EBL), neurological status, and complications. Materials and Methods Retrospective chart review of all preoperative spine tumour embolization procedures performed over 11 years by a single operator (2007-2018) at Vancouver General Hospital, on 40 consecutive patients (mean age 58; 77.5% males) with 42 embolization procedures, of which surgery was done en bloc in 22 cases and intralesional in the remaining 20. A multivariable negative binomial regression model was fit to examine the association between EBL and surgery type, tumour characteristics, embolization completeness and operative duration. Results Among intralesional surgeries, complete versus incomplete embolization was associated with reduced blood loss (772 vs 1428 mL, P < 0.001). There was no statistically significant difference in neurological outcomes or complications between groups. Highly vascular tumours correlated with greater blood loss than their less vascular counterparts, but tumour location did not have a statistically significant effect. Conclusion This study provides early evidence in support of our hypothesis that complete as opposed to incomplete tumour embolization correlates with reduced blood loss in intralesional surgeries. Randomized control trials with larger samples are necessary to confirm this benefit and to ascertain other potential clinical benefits.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".