History of antibiotic use and risk of non‐Hodgkin's lymphoma (NHL)
Bibliographic record
Abstract
A population-based, incidence case-control study was conducted among women in upstate New York to determine whether histories of certain infections and antibiotic use are associated with risk of non-Hodgkin's lymphoma (NHL). Our study involved 376 cases of NHL identified through the New York State Cancer Registry and 463 controls selected from the Medicare beneficiary files and state driver's license records. Information about use of common medications including antibiotics, history of selected infectious diseases and potential confounding variables was obtained by telephone interview. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using an unconditional logistic regression model. There was a progressive increase in risk of NHL with increasing frequency and duration of systemic antibiotic use, as assessed over the period of 2-20 years before the interview. The ORs for the highest exposure categories, >/=36 episodes and >/=366 days of use, were 2.56 (95% CI 1.33-4.94) and 2.66 (95% CI 1.35-5.27), respectively. These associations were primarily due to antibiotic use against respiratory infections and dental conditions. Moreover, the association with frequency of antibiotic use for respiratory infections was pronounced for marginal zone B-cell lymphoma and for respiratory tract lymphoma. Analyses by class of antibiotics did not suggest that a general cytotoxic effect of antibiotics was responsible for these increased risks. Although recall bias and selection bias remain potential concerns in our study, the results are generally consistent with the hypothesis that persistent infection/inflammation predisposes individuals to the development of NHL. However, a direct role of antibiotics in NHL induction has not been ruled out.
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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 | 0.001 | 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) | 0.001 | 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".