The utility and properties of the geometric mean in the assessment of differential renal function in pediatric lasix renography
Bibliographic record
Abstract
407 Objectives: The geometric mean (GM) method is believed to improve the accuracy of assessment of differential renal function compared with the determination of differential renal function from posterior differential (PD) images alone. The aim of this work is to improve understanding of the nature of the GM correction in pediatric lasix renography and to determine how often the GM changes the categorization of renal function between impairment and normal function compared with routine PD assessment. Methods: Lasix renograms for 67 patients (median age 3 years old) with suspected renal obstruction were assessed for differential renal function by both the posterior differential method (PD) and the geometric mean method (GM) requiring the acquisition of both anterior and posterior renal images. Fifty-five patients had hydronephrotic renal enlargement and eleven patients had renal ectopia, horseshoe kidney configuration, duplicated collecting systems or bilateral ureteric enlargement. Results: When the differential renal function of the abnormal kidney was less than 50%, the mean PD value was 38.3% and the GM increased this value on average by 1.7% (p=0.01, n=41). When the differential renal function of the abnormal kidney was greater than 50% the mean PD value was 56.1% and the GM decreased this value on average by 2.2% (p=0.02, n=20). PD values less than 30% were associated with a greater increase by GM assessment (4.3%) compared with PD values within the 30% to 50% range (0.9%) (p=0.04, n=47). The geometric mean changed the classification of differential renal function from abnormal to normal in 13% of cases and from normal to abnormal in 7% of cases. Conclusions: GM correction tends to normalize the differential renal function by shifting it closer toward 50% compared with the PD measurement. The magnitude of the difference between the GM and the PD values tends to be greater when renal function demonstrates a greater degree of impairment. One fifth of cases resulted in a change in categorization of renal function by use of the GM to correct the PD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".