Assessing the impact of arsenic, lead, mercury, and cadmium exposure on glycemic and lipid profile markers: A systematic review and meta-analysis protocol
Bibliographic record
Abstract
The toxicity of metals presents a significant threat to human health due to the metabolic changes they induce. Thus, it is crucial to understand the impact of exposure to toxic elements on glycemic and lipid profiles. To this end, we developed a systematic review protocol registered in PROSPERO (CRD42023393681), following PRISMA-P guidelines. This review aims to assess environmental exposure to arsenic, cadmium, mercury, and lead in individuals aged over ten years and elucidate their association with glycemic markers such as fasting plasma glucose, glycated hemoglobin, as well as lipid parameters including total cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein cholesterol. Articles published in the MEDLINE (PubMed), EMBASE, Web of Science, LILACS, and Google Scholar databases until March 2024 will be included without language restrictions. The modified Newcastle-Ottawa scale will be employed to assess the quality of the included studies, and the results will be presented through narrative synthesis. If adequate data are available, a meta-analysis will be conducted. This review can help understand the metabolic responses to exposure to toxic elements and the associated health risks.
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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 | 0.052 | 0.086 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.021 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 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".