Should surgeons control fluoroscopy during urology procedures?
Bibliographic record
Abstract
INTRODUCTION: Our study explored the impact of switching from surgeon- to radiation technologist (RT)-controlled fluoroscopy on fluoroscopy and operative times. We also identified factors impacting fluoroscopy and operative times for ureteroscopy (URS) with laser lithotripsy. METHODS: Patients undergoing urological procedures requiring fluoroscopy six months before and after the change from surgeon- to RT-controlled fluoroscopy were identified. Median fluoroscopy and operative times were compared between cohorts. Subgroup analyses were performed based on procedure performed. A multivariate analysis identified factors associated with increased fluoroscopy and operative times for URS with laser lithotripsy. RESULTS: Overall, no difference was found between surgeon and RT cohorts for fluoroscopy (58.0 vs. 56.7 seconds; p=0.34) or operative times (39 vs. 36 minutes; p=0.14). For URS with laser lithotripsy, fluoroscopy and operative times were longer in the surgeon-controlled cohort (76.0 vs. 54.0 seconds; p<0.01 and 48 vs. 40 minutes; p<0.01, respectively). For URS only, fluoroscopy time was decreased in the surgeon-controlled cohort (47.0 vs. 73.0 seconds; p=0.01). For URS with laser lithotripsy, factors independently associated with increased fluoroscopy time were male sex, flexible URS, glidewire use, and difficult ureteric stent insertion (p<0.05). Flexible ureteroscopy, glidewire use, previous ureteric stent placement, and difficult ureteric stent insertion were independently associated with increased operative time (p<0.05). CONCLUSIONS: Fluoroscopy and operative times are not significantly influenced by who controls fluoroscopy during urologic procedures. Patients undergoing URS with laser lithotripsy have decreased fluoroscopy and operative times with RT-controlled fluoroscopy. Patients undergoing URS only have decreased fluoroscopy times with surgeon-controlled fluoroscopy.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".