Identification of geographic clustering and regions spared by cutaneous T‐cell lymphoma in Texas using 2 distinct cancer registries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Cutaneous T-cell lymphomas (CTCLs) (mycosis fungoides and its leukemic variant, Sezary syndrome) are rare malignancies. Reports of the occurrence of mycosis fungoides in married couples and families raise the possibility of an environmental trigger for this cancer. Although it has been suggested that CTCL arises from inappropriate T-cell stimulation, to the authors' knowledge no preventable trigger has been identified to date. METHODS: Using region, zip code, age, sex, and ethnicity, the authors analyzed the demographic data of 1047 patients from Texas who were seen in a CTCL clinic at The University of Texas MD Anderson Cancer Center during 2000 through 2012 (the MDACC database) and 1990 patients who were recorded in the population-based Texas Cancer Registry between 1996 and 2010. Subsequently, data from both databases were cross-analyzed and compared. RESULTS: The current study findings, based on the MDACC database, documented geographic clustering of patients in 3 communities within the Houston metropolitan area, in which CTCL incidence rates were 5 to 20 times higher than the expected population rate. Analysis of the Texas Cancer Registry database defined the CTCL population rate for the state to be 5.8 cases per million individuals per year (95% confidence interval, 5.5-6.0 per million individuals per year), thus confirming the observations from the MDACC database and further highlighting additional areas of geographic clustering and regions spared from CTCL in Texas. CONCLUSIONS: The current study documented geographic clustering of CTCL cases in Texas and argued for the existence of yet unknown external causes/triggers for this rare malignancy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it