Challenges in defining thresholds for health effects: some considerations for asbestos and silica
Bibliographic record
Abstract
This paper summarizes several presentations in the Thresholds in Epidemiology and Risk Assessment session at the Monticello III conference. These presentations described evidence regarding thresholds for particles, including asbestos and silica, and cancer (e.g., mesothelioma) and noncancer (e.g., silicosis) endpoints. In the case of exposure to various types of particles and malignancy, it is clear that even though a linear non-threshold model has often been assumed, experimental and theoretical support for thresholds exist (e.g., through particle clearance, repair mechanisms, and various other aspects of the carcinogenic process). For mesothelioma and exposure to elongate mineral particles (EMPs), there remains controversy concerning the epidemiological demonstration of thresholds. However, using data from the Québec mining cohort studies, it was shown that a "practical" threshold exists for chrysotile exposure and mesothelioma. It was also noted that, in such evaluations, measurement error in diagnosis and exposure assessment needs to be incorporated into risk analyses. Researchers were also encouraged to use biobanks that collect specimens and data on mesothelioma to more precisely define cases of mesothelioma and possible variants for cases of all ages, and trends that may help define background rates and distinguish those mesotheliomas related to EMP exposures from those that are not, as well as other factors that support or define thresholds. New statistical approaches have been developed for identifying and quantifying exposure thresholds, an example of which is described for respirable crystalline silica (RCS) exposure and silicosis risk. Finally, the application of Artificial Intelligence (AI) to considering the multiple factors influencing risk and thresholds may prove useful.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".