Does the Radiologic Technologist or the Fluoroscopy Time Affect Treatment Success with Shockwave Lithotripsy?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The minimally invasive nature and effectiveness of shockwave lithotripsy (SWL) has made it one of the primary treatment modalities for urinary tract calculi. Several factors determining the success of SWL treatment have been studied, including stone factors (ie, location, size, and composition) and patient factors (ie, patient habitus and skin-to-stone distance). Our objective was to determine if either the assisting radiologic technologist or the amount of fluoroscopy time used has an impact on SWL success. PATIENTS AND METHODS: We compared the outcome of 536 SWL treatments across three radiologic technologists. We also evaluated the average amount of fluoroscopy time used in treatment success vs failures in this same cohort. The outcomes measured were stone-free and successful fragmentation rate at 2 weeks and 3 months. Successful fragmentation was defined as being either stone free, having residual sand, or with an asymptomatic fragment ≤ 4 mm on radiography of the kidneys, ureters, and bladder. RESULTS: The patients treated by the three different radiologic technologists were comparable with respect to body mass index, stone side and location, presence of ureteral stent, and mean stone area (mm(2)). The stone-free and successful fragmentation rates at 2 weeks and 3 months between the three radiologic technologists were not significantly different. When examining fluoroscopy time, we found a significantly greater mean fluoroscopy time was used in treatments with successful fragmentation at 2 weeks (3.16 min vs 2.72 min, P=0.0001) and 3 months (3.12 min vs 2.75 min, P=0.0015) compared with treatment failures. CONCLUSION: The radiologic technologist did not have a significant impact on SWL treatment outcome at 2 weeks and 3 months. Successful SWL fragmentation at 2 weeks and 3 months, however, was associated with a greater amount of fluoroscopy time, suggesting that using fluoroscopy to ensure accurate targeting during SWL is important for successful fragmentation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".