Monogenic features of urolithiasis: A comprehensive review
Bibliographic record
Abstract
Objective: Urolithiasis formation has been attributed to environmental and dietary factors. However, evidence is accumulating that genetic background can contribute to urolithiasis formation. Advancements in the identification of monogenic causes using high-throughput sequencing technologies have shown that urolithiasis has a strong heritable component. Methods: This review describes monogenic factors implicated in a genetic predisposition to urolithiasis. Peer-reviewed journals were evaluated by a PubMed search until July 2023 to summarize disorders associated with monogenic traits, and discuss clinical implications of identification of patients genetically susceptible to urolithiasis formation. Results: Given that more than 80% of urolithiases cases are associated with calcium accumulation, studies have focused mainly on monogenetic contributors to hypercalciuric urolithiases, leading to the identification of receptors, channels, and transporters involved in the regulation of calcium renal tubular reabsorption. Nevertheless, available candidate genes and linkage methods have a low resolution for evaluation of the effects of genetic components versus those of environmental, dietary, and hormonal factors, and genotypes remain undetermined in the majority of urolithiasis formers. Conclusion: The pathophysiology underlying urolithiasis formation is complex and multifactorial, but evidence strongly suggests the existence of numerous monogenic causes of urolithiasis in humans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.004 | 0.001 |
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".