Impact of Dose Hot Spots on Spinal Cord Tolerance following Stereotactic Body Radiotherapy: A Generalized Biological Effective Dose Analysis
Bibliographic record
Abstract
The purpose of this study was to investigate the effects of high-dose inhomogeneous irradiation to small volumes of spinal cord with a new generalized biological effective dose (gBED) analysis for spine stereotactic body radiotherapy (SBRT). The gBED was applied to spinal cord dosimetric data (contoured per the thecal sac) at specified volumes for a cohort of five patients with radiation-induced myelopathy (RM) and compared to nineteen patients without RM post-SBRT. The spinal cord gBED was calculated and normalized to a conventional 2-Gy equivalent dose fraction scheme (α/β = 2 Gy for late toxicity). Differences between the conventional BED and those gBED calculations by accounting for small-volume dosing within the spinal cord was observed. Statistically significant differences in the mean gBED between the RM group and the non-RM group was observed both at the maximum point volume (gBED of 66 Gy vs. 37 Gy (p = 0.01), respectively) and at the 0.1 cm(3) volume (gBED of 53 Gy vs. 28 Gy (p = 0.01), respectively). No significant difference at the 0.1 cm(3) volume was observed based on the mean BED comparisons. No significant differences were observed at the larger 1 cm(3), 2 cm(3) or 5 cm(3) volumes for either BED or gBED comparisons. We conclude that differences in dose hot spots characteristics within small inhomogenously irradiated volumes of spinal cord can affect spinal cord tolerance following SBRT treatments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".