Poster - Thur Eve - 73: Follow up of a new bladder preparation protocol when treating the prostate
Bibliographic record
Abstract
Background and Purpose: Patients treated for their prostate cancer were put on a bladder preparation protocol to obtain a full bladder. The associated high degree of discomfort forced us to change our practice. The patients were asked to drink 300ml prior to their treatment instead of 750ml. Following this change, a retrospective study was undertaken to assess the dosimetric impact on the bladder. Methods: The first group (patients who drank 750ml) had 10 patients. The second group (patients who drank 300ml) had 14 patients. Planning CT and 8 daily CBCT bladder volumes and DVHs were compared for both groups. Dose optimization constraints where based on PROFIT 78Gy/39 fraction arm. Results: For patients who drank 750ml, the average bladder volume was 377.1±60.9 cc compared with 236.5±31.5 cc for patients who drank 300 ml (p=0.043). Therapists do not report any case of incontinence in the 300 ml group. The achieved bladder wall constraint for V68% went from 23.7±1.7% to 33.8±2.0%, p=0.012, and the V91% went from 14.2±1.1% to 21.8±1.6%, p<0.001. Conclusions: A bladder filling protocol asking patients to drink less before treatment was successfully implemented in our center. We were able to eliminate incontinence during treatment and increase patient comfort. The smaller bladders lead to an increase in dose to the bladder wall, however IMRT optimization was able to produce plans that spared the bladder and were able to pass published protocol guidelines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".