#1128 Accuracy of shorter iohexol glomerular filtration rate measurement protocols in individuals with preserved kidney function
Bibliographic record
Abstract
Abstract Background and Aims Measuring glomerular filtration rate (mGFR) using exogenous tracers is recommended in a number of settings with plasma one-compartment multi-sample protocols (MSP) being the most commonly used and iohexol being the dominant tracer. The accuracy of MSP protocols has mostly been evaluated in the setting of reduced GFR where delayed initial and final samples are recommended. Much less is known about MSPs when GFR is not decreased and the default protocol tends to include initial sampling at 120 minutes and final sampling at 240 minutes post iohexol injection. The objective of this study was to assess the performance of shorter MSPs with earlier initial (60 and 90 minutes) and final (150, 180, and 210 minutes) sampling times in individuals with preserved GFR. Method Participants were recruited from a 4-year prospective, observational study evaluating the cardiovascular, renal, and bone health profile of young adults with Type 1 Diabetes. The reference mGFR was calculated using a plasma iohexol clearance one compartment slope intercept method with 5 samples collected between 120-240 minutes post iohexol injection. Four different combinations of shorter sampling strategies were investigated: 60-150 min, 60-180 min, 90-180 min and 90-210 min. Performance was evaluated using measurements of bias, precision, and accuracy (P2, P5, and mean absolute error). Results Mean age of the 43 participants was 23.9 ± 1.9 yrs with a mean eGFRCKiD-1 of 95.5 ± 15.9 ml/min/1.73 m2 and mean reference mGFR (120-240 min) of 102.3 ± 13.7 ml/min/1.73 m2. All exploratory shorter mGFRs performed well, with median biases less than 1 ml/min/1.73 m2 and mean absolute error less than 1.6 ml/min/1.73 m2. All shorter mGFRs were within 5% of the reference mGFR and the majority were within 2%. Conclusion These results demonstrate that shortening the mGFR procedure in individuals with preserved GFR provided very similar results to the current standard while significantly decreasing procedure time. The cost savings and improved feasibility of these shortened protocols should reduce some of the barriers to more widespread adoption of GFR measurement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".