Measurement of beryllium in lung tissue of a chronic beryllium disease case and cases with sarcoidosis
Bibliographic record
Abstract
BACKGROUND: The clinical features of chronic beryllium disease (CBD) are similar to many other chronic lung diseases. In particular, it may be difficult to distinguish it from pulmonary sarcoidosis since the two conditions may be very alike in clinical, pathological and radiological features. Aim To determine if the amount of beryllium found in the lungs could be used to differentiate CBD from sarcoidosis and controls. METHODS: Analyses for beryllium in the autopsied lung tissues of 29 cases and controls were carried out. The cases included one CBD, three confirmed sarcoidosis and 25 controls. Blocks of formalin-fixed tissues were analysed by an atomic absorption spectrophotometer equipped with a graphite furnace. A method for analysis of beryllium in air was modified to permit tissue analysis. RESULTS: The CBD case had a much higher average beryllium level, but some individual results were similar to controls and patients with sarcoidosis. CONCLUSION: The CBD case had beryllium levels within the range of values reported in the literature. The differentiation between CBD and sarcoidosis could not be made with reasonable assurance based only on the analytic result. Occupational history is very important in making a diagnosis of CBD, along with the analysis of tissues. Tissue analysis helped confirm the diagnosis of compensatable CBD in this particular case.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".