Redefining the non-communicable disease framework to a 6 × 6 approach: incorporating oral diseases and sugars
Bibliographic record
Abstract
The WHO Global Oral Health Status Report, published in 2022, highlighted the alarming state of oral health worldwide and called for urgent action by integrating oral health into non-communicable diseases (NCDs) and universal health coverage initiatives. 3·5 billion people have oral diseases, surpassing all other NCDs combined. The detrimental role of sugars as a risk factor for oral diseases and other NCDs has also been well documented. Despite the evidence, oral diseases and sugars are not part of the current NCD framing, which focuses on five diseases and five risk factors (ie, 5 × 5). Oral diseases and sugars remain sidelined, disproportionately affecting poor and disadvantaged populations. In this Viewpoint, we advocate for the integration of oral diseases and sugars into the current approach towards the prevention and control of NCDs. An expanded 6 × 6 framework would recognise growing evidence and would reiterate the need to strengthen action, resource allocation, and policy development for NCDs. We present the evidence and rationale for, and benefits of, an expanded NCD framework and detail recommendations to guide efforts towards improved priority, investment, and equitable health outcomes for NCDs, including oral health.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| 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".