Suboptimal Oral Health and Cardiovascular Disease: Towards an Organized Research Field and Informed Preventive Strategies
Bibliographic record
Abstract
Examining associations between suboptimal oral health (SOH) and cardiovascular disease (CVD) has formed a broad and heterogenous epidemiological field with unclear implications and persistent methodological gaps. The objectives of this dissertation are to: (1) systematically map clinical heterogeneity and selected methodological gaps in the existing literature, (2) assess the relevance of SOH to the risk of CVD when defined within overarching concepts, (3) understand the associations between SOH and three competing CVD outcomes (ischemic heart disease (IHD), stroke, revascularization), and (4) simultaneously assess the association between SOH and CVD and competing death (CD). To address the first objective, a mapping review of longitudinal studies in this area was conducted. To address the remaining objectives, three 9.6-year-median follow-up data linkage studies that included 36,176 Ontario residents aged 40 years were completed. The main oral indicator was self-rated oral health (SROH), which was assessed as excellent, very good, good, fair, poor, with SROH being defined with respect to self-rated health (SRH) to address the second objective. Participants were followed until they experienced the event of interest, loss of eligibility to healthcare, death, or end of study (December 31st, 2016). Multivariable survival models were adjusted for socioeconomic characteristics, behavioural factors, and intermediate health outcomes. The mapping review showed a severe form of clinical heterogeneity and underutilization of the examined methodological approaches, including the use of randomized controlled trials, time-varying exposures, propensity methods, mediation analysis, and competing risks analysis. The three data linkage studies indicated that: SROH may capture additional information about the risk of CVD when considered within the overarching concept of SRH; the associations between SROH and IHD and revascularization are largely explained by confounders, but not with stroke; and the association between SROH and CD is more pronounced than its association with CVD. In conclusion, this work indicates the need for: an interdisciplinary approach to refine the conceptualization of SOH in relation to CVD, closing critical methodological gaps, and mitigating the risk of CVD associated with SOH in the broader context of health promotion.
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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 | 0.148 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".