Cancer incidence attributable to insufficient fibre consumption in Alberta in 2012
Bibliographic record
Abstract
BACKGROUND: Insufficient fibre consumption has been associated with a increased risk of colorectal cancer. The purpose of this study was to estimate the proportion and absolute number of cancers in Alberta that could be attributed to insufficient fibre consumption in 2012. METHODS: The number and proportion of colorectal cancers in Alberta attributable to insufficient fibre consumption were estimated using the population attributable risk. Relative risks were obtained from the World Cancer Research Fund's 2011 Continuous Update Project on colorectal cancer, and the prevalence of insufficient fibre consumption (< 23 g/d) was estimated using dietary data from Alberta's Tomorrow Project. Age- and sex-specific colorectal cancer incidence data for 2012 were obtained from the Alberta Cancer Registry. RESULTS: Between 66% and 67% of men and between 73% and 78% of women reported a diet with insufficient fibre consumption. Population attributable risk estimates for colorectal cancer were marginally higher in men, ranging from 6.3% to 6.8% across age groups, whereas in women they ranged from 5.0% to 5.5%. Overall, 6.0% of colorectal cancers or 0.7% of all cancers in Alberta in 2012 were estimated to be attributable to insufficient fibre consumption. INTERPRETATION: Insufficient fibre consumption accounted for 6.0% of colorectal cancers in Alberta in 2012. Increasing fibre consumption in Alberta has the potential to reduce to the future burden of colorectal cancer in the province.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".