Fellowship training in endourology: Impact on percutaneous nephrolithotomy access patterns
Bibliographic record
Abstract
INTRODUCTION: Percutaneous nephrolithotomy (PCNL) remains the treatment of choice for kidney stones larger than 2 cm. Few studies have examined the reasons why some urologists obtain their own PCNL access while others prefer to have interventional radiology (IR) obtain access. The objective of this study was to investigate what factors influence this decision. METHODS: A survey was posted to the American Urological Association's (AUA) Young Urologist Community. Descriptive statistics and exploratory analyses were used to summarize practice trends and motivating factors. RESULTS: All 99 respondents began practicing within the past 11 years. Ninety-two currently perform PCNLs and 47% of them obtain their own access. Endourology fellowship-trained physicians were more likely to currently obtain their own access (75%) compared to urologists who completed non-endourology fellowships (75% vs. 23%, p=0.58) and non-fellowship-trained urologists (75% vs. 45%, p=0.01). Logging >50 cases during training also predicted physicians obtaining their own access and having a larger annual number of PCNL cases. The most common motivator for obtaining one's own access was preference to control their own access point (95%). CONCLUSIONS: Urologist-obtained PCNL access was associated with greater training experience (endourology fellowship) and current annual PCNL case volume. Urologist-reported factors that influenced the decision to obtain one's own access include control of access, comfort level, and both physician and patient convenience. By identifying the factors that influence practice patterns, we may better address barriers, improve education to make urologistobtained PCNL access feasible even without fellowship training, and ultimately improve outcomes and quality of care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".