De‐indexed estimated glomerular filtration rates: A simple step towards improving accuracy of drug dosing of renally excreted medications in moderate to severe obesity
Bibliographic record
Abstract
ABSTRACT Kidney function is underestimated in obese individuals when standard equations are applied. Laboratory‐reported estimated glomerular filtration rates (eGFR) report glomerular filtration rates corrected for body surface area in mL/min per 1.73 m2 using modification of diet in renal disease or the chronic kidney disease‐Epidemiology Collaboration equations. This may result in premature discontinuation or reduction in dosage of renally excreted medications. Currently, there are no clinical guidelines defining thresholds beyond which physicians should consider de‐indexing patient eGFR values. We compared standard and de‐indexed eGFR values for 281 consecutive patients seen in our chronic kidney disease clinic. In our study, half of the patients with a body mass index above 35 had clinically significant changes in their eGFR, with an improvement in chronic kidney disease stage, when eGFR was de‐indexed. We propose that eGFR de‐indexing should be considered in patients with moderate to severe obesity when calculating the dose, especially for medications that are excreted by the kidneys.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".