Can the sustainable development goal 9 support an untreated early childhood caries elimination agenda?
Bibliographic record
Abstract
BACKGROUND: Early childhood caries (ECC) is a global public health challenge that requires innovation, infrastructure, and health system influences to bolster initiatives for its management and control. The aim of this scoping review was to investigate the published evidence on the association between ECC and the targets of the Sustainable Development Goal 9 (SDG9) concerned with industry, innovation, and infrastructure development. METHODS: The scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. A search was conducted in PubMed, Web of Science, and Scopus between July and August 2023 using a search strategy related to the promotion of resilient infrastructure, sustainable industries, scientific research and innovation, access to the internet and ECC. Only English language publications were included. Studies that solely examined ECC without reference to the SDG9 targets were excluded. RESULTS: The search yielded 933 studies for review. After screening for the eligibility and removing duplicates, 916 unique articles remained for further screening. However, none of the identified studies provided data on the association between resilient infrastructure, sustainable industries, scientific research and innovation, access to the internet and ECC. CONCLUSION: There were no primary studies that assessed the association between ECC and SDG9, even though the plausibility of a potential relationship exists. Future studies are needed to generate evidence on the link between ECC and SDG9 as this link may contribute to the reduction in the proportion of children with untreated ECC.
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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 | 0.029 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".