Common risk factors for major noncommunicable disease, a systematic overview of reviews and commentary: the implied potential for targeted risk reduction
Bibliographic record
Abstract
Noncommunicable disease now contributes to the World Health Organization top 10 causes of death in low-, middle- and high-income countries. Particular examples include stroke, coronary heart disease, dementia and certain cancers. Research linking clinical and lifestyle risk factors to increased risk of noncommunicable disease is now well established with examples of confirmed risk factors, including smoking, physical inactivity, obesity and hypertension. However, despite a need to target our resources to achieve risk reduction, relatively little work has examined the overlap between the risk factors for these main noncommunicable diseases. Our high-level review draws together the evidence in this area. Using a systematic overview of reviews, we demonstrate the likely commonality of established risk factors having an impact on multiple noncommunicable disease outcomes. For example, systematic reviews of the evidence on physical inactivity and poor diet found each to be associated with increased risk of cancers, coronary heart disease, stroke, diabetes mellitus and dementia. We highlight the potential for targeted risk reduction to simultaneously impact multiple noncommunicable disease areas. These relationships now need to be further quantified to allow the most effective development of public health interventions in this area.
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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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".