Mode of Action: Oxalate Crystal-Induced Renal Tubule Degeneration and Glycolic Acid-Induced Dysmorphogenesis—Renal and Developmental Effects of Ethylene Glycol
Bibliographic record
Abstract
Ethylene glycol can cause both renal and developmental toxicity, with metabolism playing a key role in the mode of action (MOA) for each form of toxicity. Renal toxicity is ascribed to the terminal metabolite oxalic acid, which precipitates in the kidney in the form of calcium oxalate crystals and is believed to cause physical damage to the renal tubules. The human relevance of the renal toxicity of ethylene glycol is indicated by the similarity between animals and humans of metabolic pathways, the observation of renal oxalate crystals in toxicity studies in experimental animals and human poisonings, and cases of human kidney and bladder stones related to dietary oxalates and oxalate precursors. High-dose gavage exposures to ethylene glycol also cause axial skeletal defects in rodents (but not rabbits), with the intermediary metabolite, glycolic acid, identified as the causative agent. However, the mechanism by which glycolic acid perturbs development has not been investigated sufficiently to develop a plausible hypothesis of mode of action, nor have any cases of ethylene glycol-induced developmental effects been reported in humans. Given this, and the variations in sensitivity between animal species in response, the relevance to humans of ethylene glycol-induced developmental toxicity in animals is unknown at this time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.005 | 0.004 |
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".