Learning curve for TIP urethroplasty: A single-surgeon experience
Bibliographic record
Abstract
INTRODUCTION: We evaluate the influence of surgeon experience and other clinical factors on the success of primary hypospadias repair, using the tubularized incised plate urethroplasty (TIPU) technique. METHODS: We retrospectively reviewed pediatric cases of primary hypospadias repair performed by a single pediatric urologist (soon after his fellowship training) using TIPU between July 2002 and January 2011. The surgical techniques (including the fact that the procedure was an outpatient one) were the same for each patient. The overall complication rate (CR) was analyzed for the following factors: patient age, use of a stent, meatal position, and surgeon experience. All significant covariates on univariate analysis or with a clinical relevance were entered into a multivariable logistic regression model. A non-linear model was created to estimate the change in the CR over the years. RESULTS: Pediatric patients (median age 1.4 years old) presenting with distal (n = 251), midshaft (n = 22) or proximal (n = 30) hypospadias and with a minimum 6-week follow-up (median 13 months) were included. Most patients (87%) had a urethral stent postoperatively (mean duration 9.8 days). In total, 96 patients had 133 complications: 27 meatal stenosis, 25 meatal coronal migrations, 22 urethrocutaneous fistulas and 59 other complications. Of these, 53 patients underwent a second operation. On multivariate analysis, the only factor increasing the CR was a non-distal meatus. The non-linear model demonstrated a significant learning curve with a decreasing CR over the years. The limitations of this study are its retrospective nature and lack of long-term follow-up. CONCLUSIONS: When using TIPU, the CR significantly increases as the meatal position gets more proximal; the learning curve stabilizes after about 50 to 75 cases.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".