Effective mitigation strategies for reducing workers’ exposure to formaldehyde: a systematic review
Bibliographic record
Abstract
Abstract Formaldehyde is a toxic and carcinogenic compound, still used in several occupational settings due to its properties. Thus, in these working scenarios, it is necessary to provide effective measures to reduce workers’ exposure to formaldehyde. The aim of this systematic review is to provide a picture of the worldwide mitigation strategies implemented in occupational environments for minimizing the exposure to formaldehyde and which ones are the most effective for this purpose. The systematic review was performed according to PRISMA statement; the protocol was registered in PROSPERO (CRD42022302207). The search was performed on three electronic databases (PubMed, Scopus, and Web of Science). Studies were considered eligible if they describe strategies for mitigating formaldehyde occupational exposure and their efficacy. We included articles reporting observational studies, semi-experimental, and experimental studies and published in the English language, from the inception to March 26th, 2023. The quality assessment was performed using the Newcastle–Ottawa Quality Assessment Scale. In total, 28 articles were included in the review. The employment scenarios/activities studied were human and veterinary anatomy, autopsy, histopathology or pathology laboratories, embalming procedures, hospital, operating theaters, aquaculture, textile or foundry industries, industry using 3-D printers, offices, and firefighters’ activities. Different methods have proven useful in mitigating formaldehyde exposure, such as the use of personal protective equipment, engineering control methods, organization methods, and technical strategies, with a reduction of airborne formaldehyde until to 99.6%. The highest reduction was obtained in an anatomy laboratory through locally exhausted dissection tables equipped with activated carbon filters. The specific suitable procedures should be standardized and applied in all work settings for an appropriate risk management, in order to protect the health of exposed workers.
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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) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".