Common infection-related conditions and risk of lymphoid malignancies in older individuals
Bibliographic record
Abstract
BACKGROUND: Chronic antigenic stimulation may initiate non-Hodgkin (NHL) and Hodgkin lymphoma (HL) development. Antecedent, infection-related conditions have been associated, but evidence by lymphoproliferative subtype is limited. METHODS: From the US SEER-Medicare database, 44,191 NHL, 1832 HL and 200,000 population-based controls, frequency-matched to all SEER cancer cases, were selected. Logistic regression models, adjusted for potential confounders, compared infection-related conditions in controls with HL and NHL patients and by the NHL subtypes diffuse large B-cell, T-cell, follicular and marginal zone lymphoma (MZL). Stratification by race was undertaken. RESULTS: Respiratory tract infections were broadly associated with NHL, particularly MZL. Skin infections were associated with a 15-28% increased risk of NHL and with most NHL subtypes, particularly cellulitis with T-cell lymphoma (OR 1.36, 95%CI 1.24-1.49). Only herpes zoster remained associated with HL following Bonferroni correction (OR 1.55, 95% CI 1.28-1.87). Gastrointestinal and urinary tract infections were not strongly associated with NHL or HL. In stratified analyses by race, sinusitis, pharyngitis, bronchitis and cellulitis showed stronger associations with total NHL in blacks than whites (P<0.001). CONCLUSIONS: Infections may contribute to the aetiologic pathway and/or be markers of underlying immune modulation. Precise elucidation of these mechanisms may provide important clues for understanding how immune disturbance contributes to lymphoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".