A Case of Bowen’s Disease and Small-Cell Lung Carcinoma: Long-Term Consequences of Chronic Arsenic Exposure in Chinese Traditional Medicine
Bibliographic record
Abstract
Chronic arsenic toxicity occurs primarily through inadvertent ingestion of contaminated water and food or occupational exposure, but it can also occur through medicinal ingestion. This case features a 53-year-old lifetime nonsmoker with chronic asthma treated for 10 years in childhood with Chinese traditional medicine containing arsenic. The patient was diagnosed with Bowen's disease and developed extensive-stage small-cell carcinoma of the lung 10 years and 47 years, respectively, after the onset of arsenic exposure. Although it has a long history as a medicinal agent, arsenic is a carcinogen associated with many malignancies including those of skin and lung. It is more commonly associated with non-small-cell lung cancer, but the temporal association with Bowen's disease in the absence of other chemical or occupational exposure strongly points to a causal role for arsenic in this case of small-cell lung cancer. Individuals with documented arsenic-induced Bowen's disease should be considered for more aggressive screening for long-term complications, especially the development of subsequent malignancies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".