Re‐evaluation of the dose to the cyst wall in P‐32 radiocolloid treatments of cystic brain tumors using the Dose–Point–Kernel and Monte Carlo methods
Bibliographic record
Abstract
Intracavity instillation of beta-emitting colloid pharmaceuticals is a common technique used to treat cystic brain tumors. Most of the dosimetric calculations that have been reported in the literature for this problem are based on empirical formulas derived by Loevinger. Concentration of P-32 radiolabeled solution for the delivery of a prescribed dose (200 Gy to the cyst wall) has been published previously using this formalism in what we refer to as a standard nomogram. The calculations using the Loevinger formulas for calculating the P-32 activity necessary to achieve 200 Gy at the cyst wall is re-evaluated and compared to numerically computed results based on full Monte Carlo simulations (EGSnrc) and the dose-point-kernel (DPK) integration method. For cyst diameters greater than 1 cm, the new calculations agree well with previously published results (the standard nomogram) to within a few percents. However, for cyst diameters of less than 1 cm, it is shown that the standard nomogram results underestimate the therapeutic activity by a factor of approximately 3 for very small diameters (approximately 0.2 cm). New tables based on our calculations are presented and the sources of discrepancies are identified. It is concluded that the new set of data based on our calculations should replace the standard nomogram to administer accurately the target dose to the cyst wall for the smaller diameter cysts (< 1 cm).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".