Demographic patterns of cutaneous T‐cell lymphoma incidence in Texas based on two different cancer registries
Bibliographic record
Abstract
Cutaneous T-cell lymohomas (CTCLs) are rare, but potentially devastating malignancies, with Mycosis fungoides and Sézary Syndrome being the most common. In our previous study, we identified and described regions of geographic clustering of CTCL cases in Texas by analyzing ~1990 patients using two distinct cancer registries. In the current work, we describe in detail demographic patterns for this malignancy in our study population and apply logistic regression models to analyze the incidence of CTCL by sex, race, age, and clinical stage at the time of diagnosis. Furthermore, using Fisher's exact test, we analyze changes in incidence over time in the identified Houston communities with unusually high CTCL incidence. While CTCL primarily affects Caucasian individuals >55 years old, we confirm that it presents at a younger age and with more advanced disease stages in African-American and Hispanic individuals. Also, we demonstrate a significant increase in CTCL incidence over time in the identified communities. Spring, Katy, and Houston Memorial areas had high baseline rates. Furthermore, a statistically significant disease surge was observed in these areas after ~2005. This report supplements our initial study documenting the existence of geographic clustering of CTCL cases in Texas and in greater detail describes demographic trends for our patient population. The observed surge in CTCL incidence in the three identified communities further argues that this malignancy may be triggered by one or more external etiologic agents.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".