Gravity-assisted drainage imaging in the assessment of pediatric hydronephrosis
Bibliographic record
Abstract
INTRODUCTION: As early detection of hydronephrosis increases, we require better methods of distinguishing between pediatric patients who require pyeloplasty vs. those with transient obstruction. Gravity-assisted drainage (GAD) as part of a standardized diuretic renography protocol has been suggested as a simple and safe method to differentiate patients. METHODS: Renal scans of 89 subjects with 121 hydronephrotic renal units between January 2004 and March 2007 were identified and analyzed. RESULTS: Of all renal units, 65% showed obstruction. GAD maneuver resulted in significant residual tracer drainage in eight renal units, moderate drainage in 12 renal units, and some improvement in 40 units after the GAD maneuver. Of the eight renal units with significant residual tracer drainage, only two proceeded to pyeloplasty. After pyeloplasty, nine children had improved time to half maximum (T(1/2) Max) and 13 were unchanged. CONCLUSIONS: Our study was limited due to its retrospective design and descriptive analyses, but includes a sufficient number of subjects to conclude that GAD as part of a diuretic renography protocol is an effective and simple technique that can help prevent unnecessary surgical procedures in pediatric patients.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".