SP208EFFECTS OF CIS- AND CARBOPLATIN ON DIFFERENT URINARY BIOMARKERS OF KIDNEY INJURY
Bibliographic record
Abstract
Introduction and Aims: Cis- and Carboplatin are frequently part of a cytostatic therapy and have potential nephrotoxic side effects. Early detection of renal damage is of outmost clinical relevance to prevent acute renal failure. However, the standard clinical tests for detection of kidney injury - especially serum creatinine measurements - are insensitive and detect only advanced stages of injury. Therefore, a variety of urinary biomarkers is currently under evaluation to identify biomarkers for early detection of kidney injury. Methods: Urine samples from 29 patients (M: 11, F: 18; age 64±10.1 years; BMI 29.5± 8.8) were collected before and up to 5 days after administration of Cisplatin (n=11) or Carboplatin (n=18). Patients were suffering from different carcinoma (n=20) and lymphoma (n=9). Urine samples were analyzed for a variety of renal biomarkers using clinical chemistry and Luminex-based technology. For standardization, measurements of these biomarkers were related to urine creatinine concentrations. Statistical analysis was performed using STATISTICA 12 software. For each parameter, an Omnibus test was used. Afterwards, significance of differences between post-treatment days compared to pre-treatment was analyzed by Wilcoxon-matched pairs test, and the impact of baseline characteristics was analyzed by the Mann-Whitney-U-test.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".