Dose to regions within the prostate during the learning curve phase in low dose rate (LDR) brachytherapy: Sector analysis of 71 consecutive patients at Princess Margaret Hospital.
Bibliographic record
Abstract
91 Background: It is important to evaluate the dose distribution within the prostate in highly conformal and operator dependent treatment like brachytherapy, especially in the learning curve phase when technical skill is evolving. We report detailed dosimetric analysis of prostate sectors in the initial 71 consecutive implants performed by a physician in the first year of practice. Methods: Implants were done based on a pre plan but intra-operative adjustments were made to account for actual seed positions and addition of seeds and the final dose distribution to the prostate was documented as Intra-operative (IO) plan. Post-implant assessment was at 1 month using fused MRI/CT scans. Twelve prostate sectors were generated by division into 4 quadrants (anterior, posterior, laterals) and 3 segments (base, middle and apex). IO and post-implant (PI) V100 and D90 for sectors and whole prostate were recorded. Comparative (Wilcoxon Rank Sum) and association (Spearman's rank correlation) testing was performed for univariate analysis. Optimal dosimetry values for the entire prostate and sectors were defined as D90 >90% and V100 >85%. Results: Seventy implants were optimal (out of 71) with mean prostate PI V100 and D90 of 95.5% and 117.1%. Only 1 sector had suboptimal mean PI V100 and D90 (70.3% and 85.9%), significantly lower than IO (p<0.001), contributing to lower PI V100 for the base (88%) and anterior sectors (86.9%). The mean IO D90 of apical sectors (126%) was lower than base and mid gland (131% and 132% respectively) and lateral sectors was (134%) higher than anterior and posterior sectors (123% and 126%) consistent with a modified peripheral loading approach. D90 decreased from IO to PI plan in anterior (123% vs 106%, p<0.001) and base sectors (132% vs 108%, p<0.001) while increase was noted in apical (126% vs 133%, p<0.001) and posterior sectors (126% vs 131%, p=0.006). Conclusions: Results are encouraging with optimal dosimetry in 11/12 sectors in most patients, and sector analysis provided insight into planning factors and provided useful data for refinement in practice. Further study correlating sector analysis with clinical outcomes is required. No significant financial relationships to disclose.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".