<i>TNF</i> polymorphisms and prostate cancer risk
Bibliographic record
Abstract
BACKGROUND: Inflammation has been hypothesized to increase prostate cancer risk. Tumor necrosis factor (TNF) is an important mediator of the inflammatory process, but the relationship between TNF variants and prostate cancer remains unclear. METHODS: We examined associations between six TNF single nucleotide polymorphisms (SNPs) (rs1799964, rs1800630, rs1799724, rs1800629, rs361525, rs1800610) and prostate cancer risk among 2,321 cases and 2,560 controls from two nested case-control studies within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, n = 2,561, 5 SNPs) and the Cancer Prevention Study II Nutrition Cohort (Nutrition Cohort, n = 2,320, 6 SNPs). Odds ratios and 95% confidence intervals were estimated for individual SNPs and haplotypes in each cohort separately and in pooled analyses. RESULTS: No TNF SNP was associated with prostate cancer risk in PLCO (P-trend > or = 0.16), while in the Nutrition Cohort, associations were significant for 2 highly correlated variants (rs1799724, 1800610, r2 = 0.95; P-trend = 0.04 and 0.02, respectively). In pooled analyses, no single SNP was associated with prostate cancer risk (P-trend > or = 0.08). After adjustment for multiple testing, no SNP was associated with prostate cancer risk in either cohort individually or in the pooled analysis (P-trend all > or = 0.10). Haplotypes based on 5 TNF SNPs did not vary by case/control status in PLCO, but showed marginal associations in the Nutrition Cohort (global P = 0.06) and the pooled analysis (global P = 0.05). CONCLUSIONS: Despite somewhat suggestive haplotype results, overall our study does not support an association between TNF variants and prostate cancer 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".