Ultrasound-Based Scoring System for Indication of Pyeloplasty in Patients With UPJO-Like Hydronephrosis
Bibliographic record
Abstract
BACKGROUND: Previous scoring systems have used renal scan parameters to assess severity of ureteropelvic junction obstruction-like hydronephrosis (UPJO-like HN), however this information is not always reliable due to protocol variation across centers and renogram limitations. Therefore, we sought to evaluate the Pyeloplasty Prediction Score (PPS), which utilizes only baseline ultrasound measurements to predict the likelihood of pyeloplasty in infants with UPJO-like. METHODS: PPS was developed using three ultrasound parameters, Society of Fetal Urology (SFU) grade, transverse anteroposterior (APD) and the absolute percentage difference of ipsilateral and contralateral renal lengths at baseline. PPS was evaluated using prospectively collected prenatal hydronephrosis data (n=928) of patients with UPJO-HN. Children with vesicoureteral reflux. primary megaureter, other associated anomalies, bilateral HN and < 3 months of follow-up were excluded. Scores were analyzed regarding its usefulness in predicting which patients would be more likely to undergo pyeloplasty. Sensitivity, specificity, likelihood ratios (LR) and receiver operating characteristic (ROC) curve were determined. RESULTS: Of 353 patients, 275 (78%) were male, 268 (76%) had left UPJO-like HN, and 81 (23%) had a pyeloplasty. The median age at baseline was 3 months (IQR 1-5). The PPS system was highly accurate in distinguishing patients who underwent pyeloplasty using baseline ultrasound measurements (AUC: 0.902). PPS of 7 and 8 were found to have a sensitivity of 85% and 78%, and specificity of 81% and 90% respectively. PPS of 8 was associated with a LR of 7.8, indicating that these patients were 8 times more likely to undergo pyeloplasty. CONCLUSION: Overall, PPS could detect patients more likely to undergo pyeloplasty using baseline ultrasound measurements. Those with a PPS of 8 or higher were 8 times more likely to undergo pyeloplasty.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".