Performance of creatinine clearance equations on the original Cockcroft-Gault population
Bibliographic record
Abstract
BACKGROUND: Prediction of endogenous creatinine clearance by mathematical equations such as the Cockcroft-Gault formula is used in clinical practice in spite of the reported concern for their limited predictability. The aim of this study is to determine whether the measured creatinine clearance can be predicted accurately by a number of published equations including the recently modified Cockcroft-Gault formula = Cockcroft-Gault formula x 1.73 m2/body surface area from the original Cockcroft-Gault population. METHODS: The performance of the mathematical equations in patients with different creatinine clearance and body mass indices was assessed by computing accuracy at different percentiles, bias and precision from the original Cockcroft-Gault data. RESULTS: Refitting the modified formula to the Cockcroft-Gault data gave superior results compared to the original Cockcroft-Gault formula with an overall accuracy in the general and subgroup analysis above 70% agreement within 30% estimate of the measured creatinine clearance. On the other hand, analysis of the other equations, including the original Cockcroft-Gault, demonstrated a limited accuracy to predict creatinine clearance particularly in patients with creatinine clearance below 50 ml/min with an overall accuracy in less than 1/3 of the calculated creatinine clearance within 30% range from the measured creatinine clearance. CONCLUSION: The current creatinine clearance equations and even the original Cockcroft-Gault formula did not accurately predict the measured creatinine clearance. Normalization for body surface area in the original Cockcroft-Gault formula demonstrated more accuracy to estimate creatinine clearance, particularly in patients with diminished renal function and is recommended to physicians who wish to use the Cockcroft-Gault formula in their practice until more credible formulas are developed.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".