The dosimetric underpinning of bladder filling criteria for prostate image-guided volumetric modulated arc therapy
Bibliographic record
Abstract
OBJECTIVES: Repeated CT simulation imaging is common at our institution due to failure to achieve acceptable bladder filling in patients undergoing prostate radiotherapy. There is operational value in re-assessing the validity of the bladder filling assessment criteria by comparing the quality of two plans optimized based on either an "Accepted" or "Rejected" bladder status. METHODS: Twenty prostate patients with repeated CT simulation imaging were included. For each patient, a VMAT plan created using the "Rejected" bladder was compared with the "Accepted" bladder plan. Then, delivered dose to bladder was estimated using ≥4 CBCTs to measure number of fractions with major bladderdose violation (>5% difference) for both plans. Bladder heights of fractions without major bladder dose violations were compared to those with a violation to determine a threshold height for bladder filling acceptability. RESULTS: < 0.05). The "Rejected" bladder plans delivered a lower dose to the bladder by ≥5% than the '"Accepted" bladder plans in 59% of fractions, and the number of fractions with major dose violations was 17. CONCLUSIONS: Using a shorter bladder for plan optimization resulted in better bladder sparing during treatment and improved compliance to protocol specific bladder dose constraints. A bladder height range of 20-40 mm measured between the bladder dome and the superior aspect of the symphysis pubis is recommended for prostate radiotherapy requiring a full bladder protocol. ADVANCES IN KNOWLEDGE: Using real patient data from simulation and treatment, this study established a range of bladder height that can be measured easily in a clinical setting for assessing adequacy of bladder filling for prostate radiotherapy.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".