The Reliability of Ten-Year Dietary Recall: Implications for Cancer Research
Bibliographic record
Abstract
Remote dietary intakes may be more important than recent diet in the etiology of cancer because of the long latency in cancer development. We examined the reliability of remote dietary recall over 10 y. Subjects were 56 adults participating in a cancer prevention trial in Western Australia. All subjects completed a 28-d diet record (DR) in 1991. A food-frequency questionnaire (FFQ) modified to ask respondents about their diet 10 y earlier was sent to each subject for completion in 2001. Remote intakes recalled from 10 y earlier using the FFQ were compared with the DR using the limits of agreement (LOA) method and Pearson correlation coefficients. Mean intakes of most nutrients did not differ between dietary methods. The LOA indicated that the FFQ could under- or overestimate DR estimates by greater than or equal to50%. For many nutrients, agreement between methods depended on the magnitude of intake. Pearson's correlation coefficients ranged from 0.02 for retinol to 0.66 for alcohol. These findings are similar to those of other studies that examined the reliability of recent and remote dietary intakes. They also show that using this FFQ, remote diet recalled from 10 y earlier may be as reliable as recent dietary recall.
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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 | 0.152 | 0.355 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".