Genetically Inferred Telomere Length and Testicular Germ Cell Tumor Risk
Bibliographic record
Abstract
BACKGROUND: Studies evaluating the association between peripheral blood leukocyte telomere length (LTL) and testicular germ cell tumor (TGCT) risk have produced conflicting results. METHODS: Using available genotype data from the Testicular Cancer Consortium (TECAC), polygenic risk score and Mendelian randomization analyses of genetic variants previously associated with LTL were used to assess potential etiologic associations between telomere length and TGCT risk. RESULTS: Genetically inferred telomere length was not associated with TGCT risk among 2,049 cases and 6,921 controls with individual-level genotype data (OR, 1.02; 95% confidence interval, 0.97-1.07). Mendelian randomization analyses using summary statistic data further indicated no evidence for an association between telomere length and TGCT risk among all available TECAC participants (3,558 cases and 13,971 controls). CONCLUSIONS: Our analyses in the largest molecular genetic testicular cancer study to date provide no evidence for an association between genetically inferred peripheral blood LTL and TGCT risk. IMPACT: The lack of evidence for an overall association indicates that peripheral blood LTL is likely not a strong biomarker for TGCT risk.
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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 | 0.002 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".