Empirical model of mesothelioma potency factors for different mineral fibers based on their chemical composition and dimensionality
Bibliographic record
Abstract
Context: The potency of various mineral fiber types to produce mesothelioma was previously evaluated for numerous cohorts, but the differences in potencies for distinct fiber types have yet to be explained.Objective: To develop an empirical model that would reconstruct mesothelioma potency factors for various types of fiber based on their chemical composition and dimensionality.Methods: Typical chemical composition and dimensionality metrics (aspect ratios) were obtained and combined with mesothelioma potency factors estimated by Hodgson and Darnton method for Quebec chrysotile, South Africa amosite, South Africa and Australian crocidolite, Russian anthophyllite, Libby amphiboles, and Turkey erionite. The forward stepwise log–log regression method was utilized to determine the best combination of input parameters.Results: Mesothelioma potency factors (RM) for selected cohorts were effectively reconstructed utilizing the median aspect ratio of fibers and equivalent fractions of SiO2, total Fe oxides or total equivalent Fe3+ as Fe2O3, and MgO. Modeled potency factors increase as the aspect ratio, SiO2, and total Fe oxide (or Fe2O3) content grow, and as the MgO content diminishes. Correlation coefficients up to 0.999, p < 0.01, were achieved. The models also yield reasonable estimates of mesothelioma potencies for other fiber types, including Bolivian crocidolite, Russian chrysotile, fluoro-edenite, and others.Conclusion: In spite of the empirical approach, the proposed models provide a starting point for targeted studies of mesothelioma mechanisms by elucidating significant contributing physicochemical factors. The models have an exploratory and preliminary character but can potentially be useful to introduce quantitative structure–activity relationship approaches for the toxicology of fibrous minerals.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".