National trends of preoperative imaging modalities before partial nephrectomy for renal masses in the U.S. from 2007–2015
Bibliographic record
Abstract
INTRODUCTION: Although the performance of partial nephrectomies (PN) for renal masses has increased rapidly over the years, only a few studies have investigated the frequency and patterns of preoperative imaging modalities. The aim of this study was to investigate the frequency and patterns in preoperative imaging modalities before PN. METHODS: A total of 21 445 patients who underwent PN between 2007 and 2015 were selected from a national representative population in the MarketScan database and included in this study. The annual incidence and proportion of PN, as well as the use of each preoperative imaging modality were analyzed. RESULTS: Both annual crude number and frequency rate of PN decreased or became static since 2012. Computed tomography (CT) shows the greatest proportion of the crude number and percentage; despite a slight decrease in percentage, it is still >80%. Among the combinations, CT alone and CT combined with ultrasonography showed the highest performance rate during the complete observational period. The proportion of all other combinations, which include other complex combinations except CT alone, CT plus ultrasonography, CT plus magnetic resonance imaging (MRI), and CT plus MRI plus ultrasonography, was 13.95% in 2007, but increased to 19.04% in 2014. CONCLUSIONS: CT still plays a major role in preoperative imaging for renal masses, whereby CT alone and CT combined with ultrasonography account for a major proportion of the preoperative imaging patterns. The use of other imaging combinations, as well as renal biopsies, shows an increasing trend. Additional studies are needed to investigate whether this trend in preoperative imaging is related to the frequency rate of PN.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".