Quantitative Assessment of Elemental Carbon In The Lungs of Never Smokers, Cigarette Smokers, and Coal Miners
Bibliographic record
Abstract
Inhalation exposure to particulates such as cigarette smoke and coal dust is known to contribute to the development of chronic lung disease. The purpose of this study was to estimate the amount of elemental carbon (EC) deposits from autopsied lung samples from cigarette smokers, miners, and control subjects and explore the relationship between EC level, exposure history, and the extent of chronic lung disease. The samples comprised three subgroups representing never smokers (8), chronic cigarette smokers (26), and coal miners (6). Following the dissolution of lung tissue, the extracted EC residue was quantified using a thermal-optical transmission (TOT) carbon analyzer. Mean EC levels in the lungs of the control group were 56.68 ± 24.86 (SD) μg/g dry lung weight. Respective mean EC values in lung samples from the smokers and coal miners were 449.56 ± 320.3 μg/g and 6678.2 ± 6162 μg/g. These values were significantly higher than those obtained from the never-smoker group. EC levels in the lung and pack-years of cigarette smoking correlated significantly, as did EC levels and the severity of small airway disease. This study provides one of the first quantitative assessments of EC in human lungs from populations at high relative risk for the development of chronic lung disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".