Effect of occupational exposure to petrol and gasoline components on liver and renal biochemical parameters among gas station attendants, a review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Kidney and liver are of the most affected organs during permanent exposure to petrol and gasoline components in gas stations. This study aims to investigate the renal and liver involvements in these workers using meta-analysis. METHODS: PubMed, Scopus, Science direct, ISI web of science, and Google scholar motor engine were searched using Mesh terms of the relevant keywords. Screening of titles, abstracts and full texts was continued until the eligible articles meeting the inclusion/exclusion criteria were selected. Quality assessment was conducted using NOS (Newcastle-Ottawa Quality score). The pooled standard mean difference of the renal and liver indices between exposed/unexposed groups was estimated using Stata ver. 11 software. RESULTS: In this systematic review, 22 papers were entered. The pooled standard mean difference (95% confidence interval) between exposed and unexposed groups was estimated as of 0.74 (0.28, 1.21) for alkaline phosphatase (ALP), 2.44 (1.80, 3.08) for aspartate transaminase (AST), 2.06 (1.42, 2.69) for alanine transaminase (ALT), 0.10 (-0.09, 0.29) for total Bilirubin (TB), 0.74 (-0.42, -1.90) for total protein (TP), -0.49 (-0.82, -0.15) for albumin, 0.88 (-0.10, 1.87) for uric acid, 1.02 (0.45, 1.59) for creatinine and 1.44 (0.75, 2.13) for blood urea nitrogen (BUN). CONCLUSION: Our meta-analysis showed that the serum AST, ALT, ALP, total protein, total bilirubin, BUN, uric acid and creatinine levels were higher among workers exposed to petrol and gasoline than control group, while albumin was lower in the serum of the exposed workers. Therefore, occupational exposure to gasoline stations can create adverse effects on kidney and liver function.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.039 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".