Vascular Calcification in Animal Models of CKD: A Review
Bibliographic record
Abstract
Vascular calcification is a significant contributor to the cardiovascular mortality observed in chronic kidney disease (CKD). This review discusses the animal models (5/6 nephrectomy, mouse electrocautery model and dietary adenine) that have been employed in the study of vascular calcification outcomes in CKD. Rodent models of CKD generate a range of severity in the vascular calcification phenotype. Major limitations of the 5/6th nephrectomy model include the requirement for surgery and the need to use either excessive dietary phosphorus or vitamin D. Major limitations of the mouse electrocautery model include the requirement for surgery, the mortality rate when very advanced CKD develops, and resistance to vascular calcification without the use of transgenic animals. This is balanced against the major advantage of the ability to study transgenic animals to further understand the mechanisms associated with either the acceleration or inhibition of calcification. Dietary adenine generates severe CKD and does not require surgery. The major disadvantage is the weight loss that ensues when rats receive a diet containing 0.75% adenine. In summary, animal models are useful to study CKD-associated vascular calcification and the results obtained in these pre-clinical animal studies appear to translate to the evidence, however limited, which exists in humans with CKD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".