Evaluation of Cockcroft-Gault (CG) and abbreviated Modified Diet in Renal Disease Study (MDRD) formulas for carboplatin dosing in gynecological malignancies
Bibliographic record
Abstract
2078 Background: Carboplatin dosing is usually based on glomerular filtration rate (GFR) and area under the curve (AUC). CG and MDRD are based on serum creatinine (SrCr) to estimate GFR (eGFR) when measured GFR (mGFR) is impractical. MDRD is more accurate at low GFR in non-cancer patients and does not need body weight, but it was derived excluding patients with cancer or low albumin. We compared the accuracy of CG and MDRD in carboplatin dosing in gynecological patients. Methods: Patient data were collected retrospectively at the British Columbia Cancer Agency. Formula-derived eGFR was compared to mGFR (renogram with nuclear medicine camera and Tc-99m DTPA). eGFR-derived dose was compared to mGFR-derived dose. Bias (percentage error [PE]) and precision (absolute percentage error [APE]) were compared with 2-sided paired t-test. Results: 96 patients (70% ovarian cancer) were evaluated: median 60y, 62kg, 159cm, SrCr 71micromol/L, GFR 91mL/min, AUC 6. Both formulas had limited precision with a small bias for eGFR and dosing. [table] 85% of patients would have received a significantly different dose (APE >5%) if eGFR from either formula were used. MDRD was more precise than CG. 70% of patients would have received a significantly different dose (APE >5%) because of the difference in eGFR derived from these two formulas. Conclusion: MDRD has higher precision and similar bias compared to CG. MDRD may be an alternative to CG for carboplatin dosing in patients with gynecological malignancies. However, both formulas have limited precision as an estimate of mGFR. No significant financial relationships to disclose.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".