Split vs. Single Bolus CT Urography: Comparison of Scan Time, Image Quality and Radiation Dose
Bibliographic record
Abstract
The purpose of this study was to compare the scan time, image quality and radiation dose of CT urograms (CTU) using a split vs. single bolus contrast media injection technique. A total of 241 consecutive CTUs performed between August 2019-February 2020 were retrospectively reviewed. There were three study groups: Group 1, <50 years old, 50/80 cc split-bolus administered at 0 and 700 s post initiation of injection, with combined nephrographic and excretory phases; group 2, ≥50 years old, same split-bolus protocol; and group 3, ≥50 years old, 130 cc single bolus injection, with nephrographic and excretory phases acquired at 100 s and 460 s post injection initiation. The recorded data elements were scan time, number of excretory phases, imaging quality based on opacification of the urinary collecting system (<50%, 50–75%, 75–100%), and dose-length product (DLP). Associations between group and categorical variables were assessed (Chi-square); mean scan time and DLP were compared (one-way ANOVA). Following analysis, proportionally fewer CTUs required a repeat excretory phase in group 3 (32/112, 28.6%) than in groups 1 (25/48, 52.1%) and 2 (37/80, 46.3%) (p = 0.006). Mean scan time was significantly lower in group 3 (678 s) than in groups 1 (1046 s) and 2 (978 s) (p < 0.0001). There was no association between groups and image quality (p = 0.13). DLP was higher in group 3 (1422 ± 837 mGy·cm) than in groups 1 (1041 ± 531 mGy·cm) and 2 (1137 ± 646 mGy·cm) (p = 0.003). In conclusion, single bolus CTU resulted in significantly fewer repeat phases and faster scan time at the expense of a slightly higher radiation dose.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".