Exposure and Early Effect Biomarkers for Risk Assessment of Occupational Exposure to Formaldehyde: A Systematic Review
Bibliographic record
Abstract
The aim of the present review was to identify all the biomarkers used for assessing the internal dose and the related early effects determined by the occupational exposure to formaldehyde. For this purpose, a systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The protocol was registered in PROSPERO (ID: CRD42023416960). An electronic search of Pubmed, Scopus, and Web of Science was performed to collect all the papers concerning the focus of the review and published from the inception of each database until 18 September 2023. Articles were considered eligible if they reported data from observational studies, semi-experimental, and experimental studies on adult workers who were occupationally exposed to formaldehyde, regardless of gender or age. The quality assessment was performed using the adapted Newcastle–Ottawa Quality Assessment Scale. From 1524 articles, 52 were included. Few studies assessed the exposure to formaldehyde in occupational settings through biomarkers, especially by measuring formic acid in urine. The most common approach for evaluating the effects derived from occupational exposure to formaldehyde was the use of the cytogenetic biomarker micronucleus assay in peripheral blood lymphocytes and/or epithelial buccal cells.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".