Radiation Exposure During Videourodynamics: Establishing Risk Factors
Bibliographic record
Abstract
Objectives The use of fluoroscopy during urodynamics can be helpful in the evaluation of patients with lower urinary tract dysfunction. However, fluoroscopy introduces the potential hazards of ionizing radiation, including malignancy. In this study we analyzed the data for radiation exposure during videourodynamic study (VUDS) at our center; we have also tried to establish the factors associated with increased exposure to radiation during VUDS. Methods We reviewed all VUDS from August 2010 to May 2011. Patients were included if they were ≥18 years old and had data recorded on total radiation exposure (radcm2). Age, sex, body mass index, fluoroscopy time, diagnosis, and urodynamic findings were recorded. Multivariate linear regression analysis was used to identify independent risk factors that influenced increased radiation exposure. Results A total of 203 videourodynamic studies were assessed in 106 female and 97 male patients with a mean age of 64.3 and body mass index of 26.8. The average fluoroscopy time was 100.2 sec and exposure was 560.9 radcm2. The most common indication for videourodynamics was incontinence, 40.9%. On multivariate linear regression analysis body mass index, vesico‐ureteral reflux, sex, number of fill cycles, and larger capacity were independent predictors of increased radiation exposure. Conclusions We have shown that increased radiation exposure as measure with Dose Area Product during VUDS was significantly associated with larger BMI, female gender, larger bladder capacity, presence of VUR, junior operator, and higher number of fill cycles. Further studies are now underway to attempt to reduce exposure based on these findings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".