Development of a new equation to estimate GFR in cancer patients.
Bibliographic record
Abstract
e13503 Background: Renal function affects chemotherapy pharmacokinetics. Carboplatin dosing by Calvert’s formula is more pharmacologically rational, but requires an accurate glomerular filtration rate (GFR). Calvert argues that this requires measuring GFR (mGFR) instead of an estimated GFR (eGFR). Considering skeletal muscle is the major source for creatinine, this study looks to develop a new eGFR equation in cancer patients using lean body mass (LBM). Methods: We prospectively followed 22 stage IV cancer patients (10 female, 12 male; median age 69) who received carboplatin. mGFR by 24 hr creatinine clearance was compared to eGFR by Wright, Cockcroft-Gault (CG), CKD-EPI, MDRD and CT-determined LBM (eGFR = [Muscle Surface Area X 42]/CR). Simulated carboplatin dosing with each eGFR was then compared retrospectively in 100 Non-Small Cell Lung Cancer (NSCLC) patients for accuracy. Results: MDRD, CG, and Wright equations correlated variably with mGFR (R2 0.47, 0.57, and 0.69 respectively). Conversely, mGFR strongly correlated with LBM eGFR (R2 0.84). The Table compares eGFR calculations with mean residual error. In simulated carboplatin dosing of 100 stage IV NSCLC patients using LBM and CG eGFR, the mean residual error of the CG-determined carboplatin dose was 10% (0.5% min, max 39.7%, median 9.3%), assuming the LBM eGFR was better at estimating eGFR. This means that in approximately half of patients, carboplatin dose may be incorrect by CG if the new LBM eGFR method is truly more accurate. Conclusions: We propose a new formula for eGFR in cancer patients that appears superior to current formulas and may have implications for chemotherapy efficacy and toxicity. Studies to validate this formula are under way. [Table: see text]
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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 | 0.003 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".