MiXie, an Online Tool for Better Health Assessment of Workers Exposed to Multiple Chemicals
Bibliographic record
Abstract
There is increasing concern for workers facing multiple chemical exposure. The accumulation of information on occupational conditions indicates the need to incorporate the concept of multiple exposures in the risk assessment process and to develop tools for assessing the potential impacts of multiple exposures on workers' health. Our objective is to describe the MiXie online decision-making tool that can be used to assess the risk of exposure to multiple chemicals. The description includes the development of MiXie, the structure of its toxicological database according to the target organ or the mode of action, and the algorithm for quantitative analysis of a mixture. Two case studies of its use in evaluating the risks of multiple exposures in real workplace situations are presented. The case study in the printing industry showed increased risk for four toxicological classes (central nervous system damage, ocular damage, skin damage, and ototoxicity) associated with co-exposure to four chemicals during maintenance operations. The MiXie analysis also showed the presence of carcinogenic substances in the mixture and a risk to the development of the foetus. The case study in nail salons showed the presence of carcinogenic and sensitizing chemicals and an increased risk to upper airways. MiXie helps preventers evaluate the possible additive effects of mixtures, providing an easy-to-read diagnosis to identify risks incurred by co-exposed employees. In addition, MiXie identifies risky occupational situations that would go unnoticed without a multiple substance approach.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".