Bibliographic record
Abstract
Recent applications of robotics in the field of prostate brachytherapy are seeding the future and could potentially lead to a fully automated prostate brachytherapy surgery. Currently, a typical prostate brachytherapy surgery involves the implantation of upwards of 100 radioactive I-125 seeds by a surgeon. This review supplies background information on prostate biology, brachytherapy of the prostate, robotic brachytherapy, and transrectal ultrasound. Subsequently, it examines the physics involved in ultrasound, radiation from an I-125 source, dosimetry, and robotics. A current semi-automated robotic brachytherapy system is examined in detail and a discussion on future improvements is outlined. Finally, future work to improve prostate brachytherapy is postulated, most notably, phantom optimization using polyvinyl alcohol cryogel. The future of robotic brachytherapy lies in the advent of more sophisticated robotics. This review will give the reader a superior understanding of brachytherapy and its recent robotic advancements. Hopefully, this review will generate new ideas needed to advance prostate brachytherapy procedures leading to more accurate dosimetry, faster procedure time, less ionizing radiation received by surgery staff, more rapid patient recovery, and an overall safer procedure.
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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".