Environmental and Other Extrinsic Risk Factors Contributing to the Pathogenesis of Cutaneous T Cell Lymphoma (CTCL)
Bibliographic record
Abstract
The applications of disease cluster investigations in medicine have developed rather rapidly in recent decades. Analyzing the epidemiology of non-random aggregation of patients with a particular disease fostered identification of environmental and external exposures as disease triggers and promoters. Observation of patient clusters and their association with nearby exposures, such as Dr. John Snow’s astute mapping analysis in the mid-1800’s, which revealed proximity of cholera patients in London to a contaminated water pump infected with Vibrio cholerae, have paved the way for the field of epidemiology. This approach enabled the identification of triggers for many human diseases including infections and cancers. Cutaneous T-cell lymphomas (CTCL) represent a group of non-Hodgkin lymphomas that primarily affect the skin. The detailed pathogenesis by which CTCL develops remains largely unknown. Notably, non-random clustering of CTCL patients was reported in several areas worldwide and this rare malignancy was also described to affect multiple members of the same family. These observations indicate that external factors are possibly implicated in promoting CTCL lymphomagenesis. Here, we review the epidemiology of CTCL worldwide and the clinical characteristics of CTCL patients, as revealed by global epidemiological data. Further, we review the known risk factors including sex, age, race as well as environmental, infectious, iatrogenic and other exposures, that are implicated in CTCL lymphomagenesis and discuss conceivable mechanisms by which these factors may trigger this malignancy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".