The dosimetric impact of supplementing pre-planned prostate implants with discretionary <sup>125</sup>I seeds
Bibliographic record
Abstract
Abstract Introduction Prostate implants at the British Columbia Cancer Agency are performed using a pre-planned technique. Physicians can augment the dose distribution using one to five non-planned ‘extra’ seeds and this option is determined without intraoperative feedback. The purpose of this research is to quantify the dosimetric impact of extra seeds and to assess the circumstances under which they are considered necessary. Materials and methods Implanting physicians used a questionnaire to record the three-dimensional location and their rationale for using extra seeds. A plan reconstruction algorithm was used to distinguish the extra seeds from the planned seeds. Distributions with and without extra seeds were calculated to quantify the dosimetric impact to the prostate, urethra and rectum. Results Extra seeds resulted in mean relative increases to V100, V150 and V200 of 3·7%, 13% and 19·1%, respectively. Mean prostate D90 increased from 147 to 156 Gy. Improvements in post-implant quality assurance codes were recorded in 30% of the implants with minimal dose increase to the rectum and urethra. Extra seeds were mainly deposited in the prostate anterior–superior quadrant. Conclusions The use of two to five extra seeds can result in improvements to pre-planned prostate implants, whereas the costs in terms of increased rectal and prostatic urethral dose are relatively minor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".