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Record W4386885396 · doi:10.1016/s2468-2667(23)00205-0

Redefining the non-communicable disease framework to a 6 × 6 approach: incorporating oral diseases and sugars

2023· review· en· W4386885396 on OpenAlexaff
Habib Benzian, Abdallah S. Daar, Sudeshni Naidoo

Bibliographic record

VenueThe Lancet Public Health · 2023
Typereview
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineNon-communicable diseaseOral healthEnvironmental healthGlobal healthDiseaseIntensive care medicinePublic healthFamily medicineNursing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.951
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.242
GPT teacher head0.430
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations42
Published2023
Admission routes1
Has abstractyes

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