PULMONARY RESPONSES TO INHALED POORLY SOLUBLE PARTICULATE IN THE HUMAN
Bibliographic record
Abstract
This review focuses on the human pulmonary effects of talc, carbon black, coal dust, diesel exhaust, and titanium dioxide as examples of poorly soluble, nonfibrous particles (PSPs). Other particulates with similar characteristics (e.g., iron oxide) are included when considering the question of whether there is a characteristic human response to this class of compounds. The overall objective is to characterize the responses to these compounds using information from epidemiology, pathology, and cell biology so that comparisons with animal responses can be made. Exposure to PSPs is associated with two broad classes of disease in the human: pneumoconiosis and chronic obstructive lung disease. The basic pathologic features and pathogenesis of the different lesions associated with exposure to PSPs are described. This is followed by a review of the evidence linking exposure to coal, carbon black, diesel exhaust, talc, and titanium dioxide with these diseases. With the exception of diesel exhaust, which contains genotoxic constituents, PSPs have not been definitely linked to human lung cancer. Some questions relevant to this review are: 1. How does the human lung respond to PSPs? Specifically, (a) what are the morphologic features, and (b) what is their pathogenesis? 2. Is there evidence for clearance overload in the human, and if so, what are the characteristics of the associated tissue response? 3. Are PSPs associated with lung cancer in the human? 4. What are the major similarities and differences between human and rodent lung cancers? 5. Do inflammation, epithelial hyperplasia, and fibrosis play a role in the development of human lung cancer?
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".