Bibliographic record
Abstract
Advances in high-throughput technologies and the generation of multiomics, such as genomic, epigenomic, transcriptomic, and metabolomic data, are paving the way for the biological risk stratification and prediction of oral diseases. When integrated with electronic health records, survey, census, and/or epidemiologic data, multiomics are anticipated to facilitate data-driven precision oral health, or the delivery of the right oral health intervention to the right individuals/populations at the right time. Meanwhile, multiomics may be modified by a multitude of social exposures, cumulatively along the life course and at various time points from conception onward, also referred to as the socio-exposome. For example, adverse exposures, such as precarious social and living conditions and related psychosocial stress among others, have been linked to specific genes being switched “on and off” through epigenetic mechanisms. These in turn are associated with various health conditions in different age groups and populations. This article argues that considering the impact of the socio-exposome in the biological profiling for precision oral health applications is necessary to ensure that definitions of biological risk do not override social ones. To facilitate the uptake of the socio-exposome in multiomics oral health studies and subsequent interventions, 3 pertinent facets are discussed. First, a summary of the epigenetic landscape of oral health is presented. Next, findings from the nondental literature are drawn on to elaborate the pathways and mechanisms that link the socio-exposome with gene expression—or the biological embedding of social experiences through epigenetics. Then, methodological considerations for implementing social epigenomics into oral health research are highlighted, with emphasis on the implications for study design and interpretation. The article concludes by shedding light on some of the current and prospective opportunities for social epigenomics research applied to the study of life course oral epidemiology.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".