No role for patient body weight on renal function assessment for drug dosing
Bibliographic record
Abstract
Objectives: To evaluate the ability of body-weight-driven renal function assessment (RFA) formulae to predict on-target elimination rate ranges for gentamicin in patients with varying degrees of renal function. Methods: A 6 year retrospective pharmacokinetic study was conducted at a university teaching hospital. Results: A total of 85 patients met the inclusion criteria and 127 pharmacokinetic files were analysed from patients on medical-surgical wards (53%) and medical-surgical ICUs (13%) receiving intravenous gentamicin for treatment, as well as those for patients receiving it for surgical prophylaxis (34%). Each RFA formula was examined against standard dosing tables for gentamicin. A table of acceptable elimination rates was generated using a traditional peak of 8 mg/L and trough between 0.5 and 2 mg/L associated with each of the dosing interval extensions. The ability of each RFA formula to select on-target elimination rates was evaluated. The RFA formula assuming a normalized body weight of 72 kg and a modified creatinine reagent adjustment factor of 90% provided the most accurate on-target elimination rate selection. This method was superior to dosing interval selection based on the Modification in Diet Renal Disease (MDRD) formula, Sanford's guide method, as well as the Cockcroft-Gault formulae using total body weight, ideal body weight or lean body weight ( P < 0.0001). Conclusions: Based on the use of gentamicin as a surrogate guide for renally adjusted drugs, these results support dosing interval selection based on a normalized body weight method and a formula reagent adjustment factor of 90% within the Cockcroft-Gault formula.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".