Sci-Fri AM(2): Brachy-03: A Guide to Systematically Load Radioactive I-125 Sources in Prostate Implant
Bibliographic record
Abstract
Objectives: To develop a generalized treatment planning strategy for prostate brachytherapy, and evaluate the interdependence of the radioactive seed strength (activity), volume, implant dosimetry, and number of seeds per implant. Methods: Prostate brachytherapy is utilized in the management of clinically localized prostate cancer. It involves the trans-perineal insertion of seed-containing needles into the prostate. Prior to the implant, the patient undergoes a volume study where semi-axial images of the prostate are obtained. A physicist uses these images to come up with a plan outlining the appropriate locations of the seeds to achieve the desired implant dosimetry. The dose is related to the activity and the separation of seeds. To maintain a similar dose distribution, an increase in seed activity must be balanced by an increase in the seed spacing. To eliminate the time-consuming trial & error approach, we have come up with a set of rules to position seeds to achieve an acceptable plan. The technique has been tested on several prostates of different volumes, and the quality of the plans was evaluated. Results and Conclusions: For the cases examined so far, the technique has been efficient in determining the ideal seed locations. By appropriately increasing the activity and repositioning seeds, the total number of seeds can be reduced without appreciably altering plan quality. This will reduce material costs and the number of needles inserted, thereby increasing efficiency and lowering the associated trauma. Issues such as sensitivity of placement error need to be examined fully before the decision to increase the activity is made.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.121 | 0.094 |
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".