The Story Behind the First Mini-BEAM Photon Radiation Treatment: What is the Mini-Beam and Why is it Such an Advance?
Bibliographic record
Abstract
Radiation treatment has been the cornerstone in cancer management. However, long term treatment-related morbidity always accompanies tumor control which has significant impact on quality of life of the patient who has survived the cancer. Spatially fractionated radiation has the potential to achieve both cure and to avoid dreaded long term sequelae. The first ever randomized study of mini-beam radiation treatment (MBRT) of canine brain tumor has clearly shown the ability to achieve this goal. Dogs have gyrencephalic brains functionally akin to human brain. We here report long term follow-up and final outcome of the dogs, revealing both tumor control and side effects on normal brain. The results augur potential for conducting human studies with MBRT.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".