Bibliographic record
Abstract
Measurements of the neutron flux generated at depth in water by an 18 MV photon beam of a Varian 2100C linac are presented. The thermal neutron flux at depth exhibits a maximum at around 5 cm of 1.37/spl times/10/sup 6/ n/cm/sup 2//s for a 40/spl times/40 cm/sup 2/ field size, 100 cm SSD and a photon output of 400 MU/min. Because of the low thermal neutron flux, a large concentration of boron-10 at the tumor is required to achieve significant dose deposition by boron neutron capture reaction. To reduce the concentration required at the tumor it is necessary to increase the irradiation time without a significant increase in the photon dose. This is found to be possible by completely closing the collimator jaws which results in a 1000 times reduction in the photon dose. Also the thermal neutron flux can be further increased by reducing the SSD to the minimum possible (50 cm in the authors' case). In such conditions, the linac can be considered to be operating in a neutron beam mode. In addition, a 25 MV photon beam will generated 10 times more neutrons than an 18 MV beam. This report demonstrates that boron neutron capture therapy and boron neutron capture enhanced photon therapy can be successfully performed using the neutron beam generated by high energy medical linacs.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".