Use of polymer gel for bypassing the stereotactic imaging step prior Gamma Knife radiosurgery in small animals
Bibliographic record
Abstract
Object. Accurate targeting is crucial for the irradiation of a small volume in an animal model, such as lesions produced in the rat brain by Gamma Knife. We propose an original method based on a polymer gel dosimeter to determine the accuracy and reproducibility of irradiation using a new stereotactic frame. Methods. A in-house designed rat stereotactic frame compatible with the Gamma Knife Automatic Positioning System was constructed. Initial spatial coordinates to target the right frontal lobe were acquired by X-ray imaging of the rats positioned in the stereotactic frame using the Gamma Knife angiographic fiducial box. The rat brain was then removed through a small burr hole and the intracranial cavity was washed and filled with the polymer gel dosimeter. This gel brain was irradiated at a dose of 15 Gy using 4 or 8 mm collimator helmets. The irradiated volumes coordinates were measured non-invasively by magnetic resonance imaging (MRI) or visually after excision of the polymer gel. Results. The position of the polymerized areas revealed that the stereotactic frame is able to accurately reproduce the same position of irradiation in each animal. The average location of the center of the polymerized areas was as follows: X = 3.07 ± 0.31 mm, Y = 5.50 ± 0.26 mm and Z = 0.90 ± 0.45 mm when using 8 mm collimators; and X = 2.86 ± 0.18 mm, Y = 6.00 ± 0.22 mm and Z = 0.58 ± 0.39 mm for 4 mm collimators. The small standard deviation demonstrated that assessment of the irradiated volume performed with the gel dosimeter was highly reproducible. Conclusion. The polymer gel dosimeter confirmed the ability of the rat stereotactic frame to accurately and reproducibly position a small animal for precise radiosurgery procedures. These characteristics eliminate the need of stereotactic imaging before irradiation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".