Soluble levels of <scp>CD</scp>27 and <scp>CD</scp>30 are associated with risk of non‐<scp>H</scp>odgkin lymphoma in three <scp>C</scp>hinese prospective cohorts
Bibliographic record
Abstract
Prospective studies conducted in Western populations have suggested that alterations in soluble CD27 (sCD27) and soluble CD30 (sCD30), two markers indicative of B-cell activation, are associated with risk of non-Hodgkin lymphoma (NHL). Given that the characteristics of NHL in East Asia differ from the West and mechanistic commonalities between these populations with respect to the role of intermediate endpoint biomarkers in lymphomagenesis have not been explored, we conducted a pooled nested case-control study from three prospective studies of Chinese men and women including 218 NHL cases and 218 individually matched controls. Compared with the lowest quartile, ORs (95% CIs) for the second, third and fourth quartiles of sCD27 were 1.60 (0.83-3.09), 1.94 (0.98-3.83) and 4.45 (2.25-8.81), respectively (p(trend) = 0.000005). The corresponding ORs for sCD30 were 1.74 (0.85-3.58), 1.86 (0.94-3.67) and 5.15 (2.62-10.12; p(trend) = 0.0000002). These associations remained statistically significant in individuals diagnosed with NHL 10 or more years after blood draw. Notably, the magnitude of the associations with NHL risk was very similar to those in Western populations in previous studies. These findings of the similar association between sCD27 or sCD30 and NHL risk across different populations support an important underlying mechanism of B-cell activation in lymphomagenesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".