Heavy Metal Exposure and Renal Impairment: A Systematic Review of Observational Studies
Bibliographic record
Abstract
Background: Environmental exposure to toxins has been strongly implicated in its multi-faceted etiology of chronic kidney disease, a serious public health problem affecting individuals, families, and communities. There is a need to synthesize available studies on the effect of heavy metal exposure on renal function, considering the rising global burden of kidney disease. The objective of this study is to determine the association between exposure to heavy metals and renal disease. Methods: The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) were used to conduct the review. A comprehensive independent search, title, abstract, and full-text screening of available literature on Google Scholar, PubMed, and OAREScience was done between March 2021 and May 2021. The criteria for study inclusion were full-text articles published in English language in the last 20 years (2001-2020), and observational primary human studies reporting the association between heavy metal exposure and renal disease. The Newcastle-Ottawa Quality Assessment Scale was used to assess the quality of the included studies. Results: A total of 552 studies were identified following the search from the different databases. A total of 13 studies were finally included in the review. Heavy metals implicated in the studies include cadmium, lead, mercury, and arsenic, with ten studies showing environmental exposure as the primary source. Ten (10) studies showed an association between heavy metal exposure and renal impairment (p<0.05) while only 3 studies reported no association. Conclusion: Environmental monitoring is needed to stem the tide of heavy metal exposure in view of the growing burden of chronic kidney disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".