An analysis on control banding-based methods used for occupational risk assessment of nanomaterials
Bibliographic record
Abstract
Despite all benefits of nanomaterials, their unique characteristics made them an emerging hazard in workplaces, which need to be assessed for their potential risks. So, the aim of this study was to review all the studies conducted on the risk assessment of activities involving nanomaterials with CB-based methods.This study is based on a literature review on databases including Web of science, Scopus, PubMed, and SID. After reviewing and screening studies according to PRISMA, the collected data were meta-analyzed by Comprehensive Meta-Analysis Software. Also, Newcastle-Ottawa checklist was used for quality assessment of the studies. To determine similarity of methods, Cohen's Kappa was used. Sensitivity analysis was used to determine the role of each factor in the risk assessment by using the Crystal Ball tool.There are eight validated methods for risk assessment. Also, some authors used a self-deigned tool based on CB approach. The results of meta-analysis showed that the odds ratio for the risk of activities involved with nanomaterials was 0.654 (high risk). Results of simulation for Nanotool showed that the mean risk level of activities involved with nanomaterials, with a certainty of 95.07%, is moderate (RL3). Moreover, sensitivity analysis showed that the risk was depended on "Hazard band" in all methods except ISO method.The obtained results can be useful in improving existing methods and suggesting new methods. Also, there is a need to design and propose specific methods for risk assessment of incidental and natural nanomaterials.
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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 | 0.107 | 0.283 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.023 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".