Epidemiological studies of physical activity and diabetes risk, and implications for diabetes preventionThis paper was originally part of the Symposium “Exercise, insulin sensitivity and diabetes—what’s new?”, which appeared in the June issue of Appl. Physiol. Nutr. Metab.
Bibliographic record
Abstract
The evidence linking physical inactivity to the future risk of type 2 diabetes is strong, and modification of behaviour is a critical and effective element of strategies aimed at the prevention of this increasingly prevalent disorder. Two key unresolved epidemiologic issues relate to the type of activity that is likely to be maximally effective in preventing diabetes and the amount of activity that is required. Resolution of both these issues is likely to require a change in the way activity is measured, with a move away from self-report instruments, toward objective assessment of activity and the pattern and overall level of energy expenditure. It is also unclear whether the impact of physical activity on metabolic risk is homogenous across the population. Subgroups that might respond differently could be defined on the basis of characteristics such as age, degree of obesity, family history, ethnicity, and genetic risk, but the literature on effect modification is limited by study design issues. The identification of such subgroups could aid in the targeting of preventive interventions. An appropriate balance between individually tailored approaches aimed at those at high risk and interventions aimed at trying to shift physical activity levels in entire populations remains to be determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".