Sci—Wed PM: Delivery—05: SBRT of Spinal Lesions Utilizing the Elekta Beam Modulator, Hexapod Couch and BodyFIX Systems
Bibliographic record
Abstract
Stereotactic body radiation therapy (SBRT) of the spine involves a high biologic effective dose to the target, while sparing the adjacent spinal cord. With a prescription of 24 Gy in 2–3 fractions (25 Gy in 5 fractions for re‐treatments) and the maximum point dose to the spinal cord limited to 1000–1400 cGy, the target coverage may be compromised to respect the cord dose limit. Due to these tight margins, our centre performs SBRT with the BodyFIX immobilization system, using the Elekta Synergy Beam Modulator and cone‐beam CT (CBCT) for registration with the planning image using automatic grey value alignment. We initially used the Elekta Precise couch to setup the patient using 3 translations. The Hexapod couch is now used to precisely setup the patient using all six degrees of freedom. Intra‐fraction motion is assessed and corrected by means of a mid‐treatment CBCT. To date we have treated 13 spinal lesions with the BodyFIX system. For the 10 spinal lesions (totaling 36 fractions) treated prior to the installation of the Hexapod, the mid‐treatment translational and rotational errors (average absolute deviation) were determined to be less than 0.8 mm and 1.2 degrees. For the 3 spinal lesions (totaling 9 fractions) treated since the Hexapod installation, correction of all six degrees of freedom has resulted in mean mid‐treatment translational and rotational errors of less than 0.9 mm and 0.7 degrees. Consequently, CBCT image‐guidance and the Elekta Hexapod couch are capable of providing precise patient setup, while the BodyFIX system ensures minimal intra‐fraction motion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.332 | 0.152 |
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".