Sci—Fri PM: Delivery — 03: Spine SBRT: Treating multiple vertebrae using cone‐beam CT image‐guidance and the hexapod robotic couch
Bibliographic record
Abstract
PURPOSE: The intent of spinal SBRT is to maximize the biological effective dose and improve local control, while sparing the adjacent spinal cord. We report on the spinal SBRT positional accuracy for multiple, consecutive vertebrae in a single course, using the Elekta Synergy-S and BodyFIX immobilization systems. METHODS: After initial patient adjustment, verification cone-beam CT (CBCT) images were acquired before, during and after treatment. These images were used to assess immobilization and correct any misalignment exceeding 1 mm or 1°, in all six degrees-of-freedom using the HexaPOD robotic couch. This analysis is based on 415 verification images from 67 consecutive courses of treatment. These treatment courses comprised 25 single thoracic vertebrae, 16 multiple thoracic vertebrae, 20 single lumbar vertebrae and 6 multiple lumbar vertebrae. RESULTS: The absolute intra-fraction motion averaged over all directions (±std dev.) for the T-single, T-multiple, L-Single and L-Multiple was 0.54 (±0.73) mm, 0.54 (±0.88) mm, 0.36 (±0.57) mm, and 0.47 (±0.63) mm respectively. The percentage that exceeded the 1.5 mm planning margin was 3.8%, 4.0%, 1.0% and 0.85% respectively. T-spine treatments were out-of-tolerance more frequently than the L-spine. There was a statistically significant difference between single and multiple lumbar treatments (unpaired t-test, p<0.01), but this was not clinically significant as 99% were within our 1.5 mm margin. CONCLUSIONS: Near-rigid immobilization with the acquisition of intra-fraction CBCT images and the correction of misalignments in all six degrees-of-freedom provides the necessary precision to safely perform SBRT of consecutive spinal metastases within one course of treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.176 | 0.052 |
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".