Cigarette smoking and risk of non-Hodgkin lymphoma subtypes among women
Bibliographic record
Abstract
Previous studies of the relationship between cigarette smoking and non-Hodgkin lymphoma (NHL) have yielded conflicting results, perhaps because most studies have evaluated the risk for all NHL subtypes combined. Data from a population-based case-control study conducted among women in Connecticut were used to evaluate the impact of cigarette smoking on the risk of NHL by histologic type, tumour grade, and immunologic type. A total of 601 histologically confirmed, incident cases of NHL and 718 population-based controls provided in-person interviews. A standardised, structured questionnaire was used to collect information on each subject's current smoking status, age at initiation, duration and intensity of smoking, and cumulative lifetime exposure to smoking. Our data suggest that cigarette smoking does not alter the risk of all NHL subtypes combined. However, increased risk of follicular lymphoma appears to be associated with increased intensity and duration of smoking, and cumulative lifetime exposure to smoking. Compared with nonsmokers, women with a cumulative lifetime exposure of 16-33 pack-years and 34 pack-years or greater experience 50% increased risk (OR=1.5, 95% CI 0.9-2.5) and 80% increased risk (OR=1.8, 95% CI 1.1-3.2), respectively, of follicular lymphoma (P for linear trend=0.05). Our study findings are consistent with several previous epidemiologic studies suggesting that cigarette smoking increases the risk of follicular lymphoma. This research highlights the importance of distinguishing between NHL subtypes in future research on the aetiology of NHL.
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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 | 0.000 | 0.001 |
| 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".