Pulsed fluoroscopy in retrograde urethrograms
Bibliographic record
Abstract
OBJECTIVES: Retrograde urethrogram (RUG) is one of the corner stones for the reconstructive urologist. With hundreds of RUGs being performed yearly in busy reconstructive center, the concern for radiation exposure to the patient and the medical personnel becomes important. We propose the use of pulsed fluoroscopy to decrease the radiation exposure for patient and medical personnel. METHODS: Patients presenting to our center with urethral strictures between March 2016 and March 2019 were included in our study. The fluoroscopy machine was set for pulsed fluoroscopy at a setting of 4 pulses per second. Patient information including demographics, pre-operative diagnosis, Intra-op findings, and fluoroscopy time were recorded. RUG was performed to localize the stricture pre-operatively and post-operatively. RESULTS: A total of 185 RUG were performed between March 2016 and March 2019. The median age was 63 (14-81). The remaining 154 RUG had 77 performed pre-operatively and 77 performed post-operatively. Pathology was identified in 77 patients. Intra-operative confirmation of pre-operative finding was found in 76 patients (98.7%). Median fluoroscopy time was found to be 2.43 seconds (0.5 sec- 6.5 sec). CONCLUSIONS: Pulsed fluoroscopy reduces the radiation exposure in RUG without a reduction in the diagnostic capacity of the test. Reduction of fluoroscopy can have beneficial cumulative effect as per the ALARA principle for patients and medical personnel. Further studies with randomized control trials could be of great benefit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".