Use of artificial neural networks in the management of antenatally diagnosed ureteropelvic junction obstruction
Bibliographic record
Abstract
BACKGROUND: In this study, an artificial neural network (ANN) based system has been developed specifically to help in the management of antenatally diagnosed uretero-pelvic junction (UPJ) obstruction. METHODS: A total of 53 infants with antenatally detected hydronephrosis caused by UPJ obstruction were included in this study. A neural network was developed with the help of a commercially available software package. The patients' age and sex, renal pelvic diameter, laterality, split renal function and presence of renal scar on radionuclide scan, follow-up times, urine culture results and the presence of symptomatic infections were used as variables. These data were also entered into a statistical software package and linear regression analysis was done. RESULTS: During the follow-up period, 36 children were observed, and the remaining 17 renal units underwent pyeloplasty. The average sensitivity of the ANN model in predicting the outcome was found to be 92% in the training group and 75% in the validation and test groups. In linear regression, none of the predictors were found to be statistically significant. INTERPRETATION: In this study, we have demonstrated that the use of ANNs in antenatally diagnosed UPJ obstruction can help the clinician in making treatment decisions, and thus can be useful in daily clinical practice.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".