Bibliographic record
Abstract
To the Editor: We read with interest the article comparing volumetric modulated arc therapy (VMAT) vs Gamma Knife (GK) radiosurgery treatment planning of multiple brain metastases, in which the authors erroneously conclude “4-arc VMAT produced clinically equivalent conformity, dose falloff, 12 Gy isodose volume, and low isodose spill.”1 Their study applied different prescription isodose volumes for GK plans and VMAT plans, as is apparent in Figure 2, which shows significantly larger prescription isodose volumes for GK plans than for VMAT plans (P < .001). Had the same prescription isodose volumes been used, significantly larger normal tissue doses would have been delivered with VMAT, a result previously demonstrated in multi-institutional benchmark studies.2,3 In short, VMAT delivers faster treatment but with significantly increased normal tissue dose. Disclosures Drs Ma and Sahgal currently serve on the board of International Stereotactic Radiosurgery Society (ISRS). Dr Sahgal also holds research grants with Elekta, AB and has received honoraria for past educational seminars. Otherwise, the authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".