Impact of adherence to cancer-specific prevention recommendations on subsequent risk of cancer in participants in Alberta’s Tomorrow Project
Bibliographic record
Abstract
OBJECTIVE: The World Cancer Research Fund (WCRF) and the American Institute for Cancer Research (AICR) publish recommendations for cancer prevention. The present study aimed to estimate the association between adherence to these cancer-specific prevention recommendations and subsequent development of cancer in a prospective cohort. DESIGN: A composite adherence score was constructed based on questionnaire data to reflect overall adherence to WCRF/AICR lifestyle-related recommendations on body fatness, physical activity, diet and alcoholic drinks. Multivariable Cox proportional hazard regression models were used to assess the association (hazard ratio; 95 % CI) between the adherence score and risk of developing cancer. SETTING: Alberta's Tomorrow Project, a prospective cohort study.ParticipantsMen and women (n 25 100, mean age at enrolment 50·5 years) recruited between 2001 and 2009 with no previous cancer diagnosis were included in analyses. RESULTS: Cancer cases (n 2066) were identified during a mean follow-up of 11·7 years. Participants who were most adherent to the selected WCRF/AICR recommendations (composite score: 4-6) were 13 % (0·87; 0·78, 0·98) less likely to develop cancer compared with those who were least adherent (composite score: 0-2). Each additional recommendation met corresponded to a 5 % (0·95; 0·91, 0·99) reduction in risk of developing cancer. When stratified by sex, the associations remained significant for women, but not for men. CONCLUSIONS: Adherence to lifestyle-related cancer prevention recommendations was associated with reduced risk of developing cancer over the follow-up term in this Canadian cohort.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".