Machine learning-based prediction of vesicoureteral reflux outcomes in infants under antibiotic prophylaxis
Bibliographic record
Abstract
We aimed to investigate the independent outcome predictors of continuous antibiotic prophylaxis (CAP) in vesicoureteral reflux, train a model to predict the outcome, and evaluate which infants should be referred for endoscopic vesicoureteral reflux correction in their first visits. A total of 225 infants ≤ 2 years of age with a diagnosis of vesicoureteral reflux between 2009 and 2022 were recruited; 115 patients from a pediatric nephrology clinic received CAP, and 110 patients from a pediatric urology department underwent endoscopic injection of dextranomer/hyaluronic acid copolymer. In the multivariable analysis, only renal scarring and bladder dysfunction were significantly associated with post-treatment febrile urinary tract infections and/or renal scarring and vesicoureteral reflux persistence, respectively, in children who received CAP. The machine learning modeling showed that for both febrile urinary tract infections and/or renal scarring and vesicoureteral reflux persistence, the random forest was the best fit. On the other hand, we observed that the success rates of endoscopic injection among the patients with renal scarring and bladder dysfunction were acceptable. In conclusion, renal scarring and bladder dysfunction were predictors of vesicoureteral reflux outcomes when the infant was receiving CAP. Therefore, referring these patients to a urologist is advised during their first visits as they benefit from endoscopic injection.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".