Predictors of Successful First-Attempt Prostate Cancer Computed Tomography Simulation: A Prospective Cohort Study
Bibliographic record
Abstract
Purpose: Successful computed tomography (CT) simulation in prostate cancer radiation therapy relies on consistent bowel and bladder preparation. This study aimed to determine the first-attempt CT simulation success rate and identify factors associated with a successful simulation. Methods and Materials: This single-institution, prospective cohort study recruited patients with prostate cancer undergoing CT simulation for pelvic radiation. We abstracted the success of CT simulation on the first attempt, the number of scan attempts in a single visit, the reason for failed attempt(s), and the frequency of rescheduled appointments. Patients completed a survey regarding their preparation experiences, demographic data, and patient-reported outcomes. The primary outcome was a successful first-attempt CT scan. A generalized estimating equation model evaluated factors associated with successful first scan, including age, CT appointment time, American Urological Association urinary symptom scores, constipation, diarrhea, and instruction format. Additionally, qualitative analysis of open-text patient feedback explored barriers to effective preparation. Results: = .01) compared to verbal instructions alone. Qualitative analysis of 118 patient comments revealed common barriers, including unclear preparation instructions (23.7%), difficulty timing bowel movements (10.2%), and confusion about expectations (14.4%). Conclusions: Low CT simulation success rates emphasize the need for improved patient preparation strategies. Multimodal education significantly enhanced success rates. Addressing communication methods and, refining preparation protocols should reduce rescans, and optimize workflows.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".