Dietary Fish Intake and Risk of Leukaemia, Multiple Myeloma, and Non-Hodgkin Lymphoma
Bibliographic record
Abstract
This study aimed to determine whether fish intake was protective against leukemia, multiple myeloma, and non-Hodgkin lymphoma (NHL), and if our previous finding of a protective effect of fish-related occupations on the risk of these diseases was due to dietary intake of fish. We used data from a population-based case-control study undertaken in Canada in 1994-1998. Dietary information was available for 919 leukemia cases, 287 myeloma cases, 1418 NHL cases, and 4202 controls. The risk of each of the three cancers was determined using multiple logistic regression analysis according to quartiles of weekly fresh fish intake, percentage of total energy intake from fresh fish, and percentage of total fat intake from fresh fish. After adjusting for age, sex, smoking, BMI, and proxy status, people who consumed greater proportions of their total energy intake from fresh fish had a significantly lower risk of each of the three types of cancer, and there was a significant dose-response for risk of leukemia and NHL. Those in the highest quartile for percentage of fat intake from fish were at lowest risk: leukemia odds ratio (OR) 0.72, 95% confidence interval (CI) 0.58-0.89; multiple myeloma OR 0.64, 95% CI 0.45-0.90; NHL OR 0.71, 95% CI 0.60-0.85; and all LH cancers combined OR 0.70, 95% CI 0.61-0.81. The protective effect previously observed for working with fish on the risk of leukemia and lymphoma was independent of fish intake. These findings suggest that a diet high in fish may be protective against lymphohematopoietic cancers and confirm the reduced risk among fish workers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".