The evolution of pre-operative spine tumour embolization
Bibliographic record
Abstract
Pre-operative spine tumour embolization is a useful adjunct to minimize operative complications and blood loss during complex resections. While the efficacy of this procedure has been well studied, relatively little is documented regarding how to optimize technical parameters for tumour characteristics. This pictorial case series seeks to review our centre's experience over the last decade in using a range of embolization techniques. As experience with this procedure has matured, we propose an approach based on the patient's vascular anatomy and tumour angioarchitecture. This includes the use of coils as protective barriers rather than primary embolics; particle embolization to permeate fine capillary networks; consideration for liquid embolic agents in the presence of large caliber tumour vessels with associated arteriovenous shunting; and percutaneous intralesional embolization when endovascular access is insufficient to achieve the desired outcome. In many cases, a combination of these methods is needed, and close communication with the surgeon ensures the best outcome. Despite these advances, continued work is needed to determine how to optimize complete devascularization, and thus surgical benefit, while safely sparing critical neuroanatomical structures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".