The Toronto nomogram: A Bayesian meta‐regression derived prenatal ultrasound index to predict lower urinary tract obstruction and prune belly syndrome
Bibliographic record
Abstract
INTRODUCTION: A nomogram for predicting the diagnosis of lower urinary tract obstruction (LUTO) based on an antenatal ultrasound index generated from a Bayesian Meta-regression analysis has been in development and noted with superior diagnostic accuracy compared to the keyhole sign (KHS). We aim to assess the accuracy of the nomogram in expanded diagnostic utilization to predict LUTO. METHODOLOGY: The validation of the nomogram for expanded diagnostic utilization was based on data from a prospective institutional antenatal clinic database between January 2020 and June 2022. Diagnostic accuracy indices were determined for confirmed postnatal diagnosis of LUTO or prune belly syndrome (PBS). Receiver operating characteristics (ROC) curves were generated to compare the area under the curve (AUC) of the nomogram versus KHS. RESULTS: Based on 84 male fetuses with antenatal ultrasound of moderate-severe hydronephrosis (PUV n = 15, PBS n = 4), the KHS had 26.3% (95%CI 9.1-51.2) sensitivity and 100% (95%CI 94.4%-100%) specificity, with 14 false-negatives. The nomogram showed a 84.2 (95%CI 60.4%-96.6%) sensitivity and 95.4 (95%CI 87.1%-99%) specificity with three false-positives. The nomogram also had a superior AUC compared to KHS (0.98 vs. 0.63). CONCLUSION: The nomogram can be used as a valuable tool to trigger further postnatal screening and provide individualized risk assessments to families during prenatal counseling.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".