Self‐reported history of infections and the risk of non‐Hodgkin lymphoma: An InterLymph pooled analysis
Bibliographic record
Abstract
We performed a pooled analysis of data on self-reported history of infections in relation to the risk of non-Hodgkin lymphoma (NHL) from 17 case-control studies that included 12,585 cases and 15,416 controls aged 16-96 years at recruitment. Pooled odds ratios (OR) and 95% confidence intervals (95% CI) were estimated in two-stage random-effect or joint fixed-effect models, adjusting for age, sex and study centre. Data from the 2 years before diagnosis (or date of interview for controls) were excluded. A self-reported history of infectious mononucleosis was associated with an excess risk of NHL (OR = 1.26, 95% CI = 1.01-1.57 based on data from 16 studies); study-specific results indicate significant (I(2) = 51%, p = 0.01) heterogeneity. A self-reported history of measles or whooping cough was associated with an approximate 15% reduction in risk. History of other infection was not associated with NHL. We find little clear evidence of an association between NHL risk and infection although the limitations of data based on self-reported medical history (particularly of childhood illness reported by older people) are well recognized.
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.
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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.032 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".