The PREVIEW-Population studies: design and methods
Bibliographic record
Abstract
The overall objective of PREVIEW population studies is to substantiate the findings in the intervention study with data from five longitudinal population studies from Europe, New Zealand and Canada, covering the entire lifespan. It will be evaluated whether protein intake, glycaemic index (GI), and physical activity, are predictive of diabetes and its cardiovascular consequences. This will add insight into the natural history of diabetes, by considering specific dietary and exercise factors. The following epidemiological studies will be analysed: 1. The Netherlands: LifeLines (2006-ongoing), a three-generation population-based study in 165,000 people 4-90+y and NQplus (2011-ongoing), a survey in n=1,750 people 20-70y, repeated 3 times. 2. Finland: Cardiovascular Risk in Young Finns Study (1980-2012), a survey in n=3,596 people 3-18y at baseline, repeated 8 times. 3. New Zealand: NZ Adult Nutrition Survey (2008/09), a cross-sectional survey in n=4,721 people> 15y. 4. Canada: Quebec Family Study (1978-2002), a 3-phase longitudinal study from ~500 families including ~200 families with one obese member. A common database of these five population studies will be generated. Main exposure variables will be dietary components and physical activity. The outcome will be diabetes prevalence or incidence, and blood glucose parameters. Data-analysis will be conducted with meta-analytical techniques, using a random-effects model to consider heterogeneity among cohorts. Population attributable risks will give an estimate on how much of diabetes risk could theoretically be prevented by modifying these factors. Additionally, given that GI, one of the key exposures, is not routinely available in all food tables, a dedicated questionnaire focusing on assessing GI will be developed. It will be applied to the NQPlus cohort to gain insight into the quality of the GI results. The research described here receives funding from the EU Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 312057.
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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 | 0.066 | 0.105 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.008 |
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".