Fractionated Helical Tomotherapy as an alternative to radiosurgery in patients unwilling to undergo additional radiosurgery for recurrent brain metastases
Bibliographic record
Abstract
Our clinic routinely treats brain metastases with stereotactic radiosurgery using a 6 megavoltage (MV) linear accelerator, cones, and a surgically attached head frame. Four patients declined repeat radiosurgery for new lesions due to their previous discomfort and a fifth patient could not complete radiosurgery because of uncontrolled nausea. Instead patients were treated with Helical Tomotherapy (HT). This report discusses the spatial dose distribution of HT as measured in a head phantom and the clinical course of these five patients. The planning target volume (PTV) was a 3 mm geometric expansion of the gross tumour volume (GTV). The prescribed dose to the PTV was 27 Gy in five daily fractions with the distribution optimised to deliver 30 Gy to the GTV. Patients were immobilised with a mask and the lesions were targeted by MV computerised tomography, an inherent feature of the system. One patient died six weeks later from systemic disease; the remaining patients survived eight to 16 months. No patient experienced an exacerbation of neurological symptoms following Helical Tomotherapy. These results suggest that fractionated Helical Tomotherapy for brain metastases may be a viable alternative to radiosurgery in patients unable or unwilling to undergo that procedure.
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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 | 0.001 |
| 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) | 0.002 | 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".