Comparison of Formaldehyde Exposure Levels in Two Multi-Industry Occupational Exposure Databanks Using Multimodel Inference
Bibliographic record
Abstract
The recent increase in international multicenter epidemiologic studies of occupational hazards and the current development of risk analysis tools based on multinational exposure data provide an incentive to evaluate the extrapolation of exposure data across countries. We compared formaldehyde measurements in the French (COLCHIC) and U.S. (IMIS) occupational exposure databases for 1986-2001 using multimodel inference. In this approach, conclusions are based on a set of plausible models rather than on a single model. Modeled variables included data source, industry, year, and sample type (short-term or long-term). The model set included 72 models corresponding to testing all variables and 5 interactions and estimation of 93 coefficients. As it is impossible to classify non-detects (ND) as short-term or long-term samples, they were removed from IMIS prior to analysis. Respectively, 3143 and 2646 IMIS and COLCHIC data, spread across 28 common industries, were analyzed. The full model explained 21% of the total variability. All fixed effects and the source-industry interaction were shown as strong predictors of exposure. The temporal trend (5% yearly decrease) and difference between short-term and long-term data (short-term greater than long-term by two-fold) were similar across the two databanks. Industry-specific differences between IMIS and COLCHIC were detected only for health-related activities for which COLCHIC levels were higher (~ four-fold). Sensitivity analyses were conducted to assess the impact of excluding NDs from IMIS. They showed initial predicted industry-specific geometric means for IMIS data potentially multiplied by factors from 0.42 to 0.98 (long-term data) and from 0.11 to 1.4 (short-term data) when NDs were included with various distributions across the short-term and long-term categories. The most realistic scenario yielded 0.82 for long-term (18% overestimation) data and 1.05 (5% underestimation) for short-term data. Although tempered by a probable non-detect bias, our analysis showed that both databanks provide a similar multi-industry portrait of formaldehyde exposure despite a potential for very different occupational settings. Our results offer encouraging insight about extrapolation of exposure data across countries.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".