Segregation analysis of cancer in families of glioma patients
Bibliographic record
Abstract
A small proportion of brain tumors are attributed to a genetic predisposition; however, the hereditary proportion is undetermined. This study evaluates the degree of familial aggregation of cancer in a large series of brain tumor patients. Our study included 5,088 relatives of 639 probands (3,810 first- and 1,278 second-degree), diagnosed with a glioma between June 1992 and June 1995 at The University of Texas M. D. Anderson Cancer Center, Houston, Texas, with diagnosis under age 65 years, and residents of the United States or Canada. We conducted an in-person or telephone interview with patients and/or their next-of-kin, and obtained family histories for the probands' first-degree (parents, siblings, offspring) and selected second-degree relatives (aunts, uncles, grandparents) using a sequential sampling strategy. Reported cancers were documented by medical records and/or death certificates (if the relative was deceased and medical records were unavailable). We conducted segregation analysis using the Pedigree Analysis Program (PAP). The analyses were divided into two categories: (1) all 639 families, and (2) a subset of families whose gliomas stained positive on p53 immunohistochemistry analysis. We demonstrated that a multifactorial Mendelian model was favored, while a model postulating a purely environmental cause of brain cancer was rejected. This study indicates that familial cancer in relatives of glioma patients are probably a result of multigenic action, and familial clustering of cancer among relatives of glioma patients may involve unknown environmental exposures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".