Evaluation of Two Protocols of Uremic Rat Model: Partial Nephrectomy and Infarction
Bibliographic record
Abstract
Animal models of chronic renal failure have been mostly achieved by partial ablation of renal parenchyma, the two most common techniques employed being surgical resection or infarction. Evaluation of the uremic model using these two techniques was carried out in Wistar rats. Two weeks after operative procedure, measured serum urea levels in the resection and infarction models were 59.1 and 64.3 mg/dL (normal range 15.6-24.4 mg/dL) respectively. However, the standard deviation in the former was significantly lower, 6.3 vs. 97.1 mg/dL from infarction model, p = 0.007. A consistent degree of glomerular filtration rate reduction was obtained in the resection model, resulting in 20-30% of normal creatinine clearance. This compared favorably with the creatinine clearance range (0.3-74% of normal) from the infarction model, in which two animals died of uremia and seven had higher than 50% of normal creatinine clearance. It is reasonable to attribute reproducibility and homogeneity demonstrated in the resection model to (i) more precise control of renal ablation extent with surgical techniques and (ii) less interplay of confounding injury mechanism to remnant kidney. These data support superiority of the resection model as an experimental tool for pathophysiological and/or interventional investigations of chronic renal failure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".