Oral Health and Cardiovascular Disease: Mapping Clinical Heterogeneity and Methodological Gaps
Bibliographic record
Abstract
BACKGROUND: Numerous studies have examined the associations between poor oral health and the incidence of cardiovascular disease (CVD) over the past 25 y. This long history of research has resulted in a broad and heterogenous epidemiological field whose implications are difficult to understand and whose methodological gaps are hard to track. OBJECTIVES: This systematic mapping review aims to systematically map clinical heterogeneity and methodological gaps in assessing the relationship between poor oral health and CVD outcomes. METHODS: Medline, Embase, and Cochrane Library were searched to identify longitudinal studies that examined the relationship between any oral health indicator and CVD outcomes. Each database was searched from its inception date and June 27, 2018. Extracted data assess the clinical heterogeneity (participants' characteristics, exposure and outcome measures, length of follow-up) and methodological gaps (availability of randomized controlled trials, utilization of time-varying exposures, propensity methods, mediation analysis, and competing risks analysis). RESULTS: Eighty-five studies met the inclusion criteria. Clinical heterogeneity is evident in participants' characteristics (age, clinical status, and occupation) and in the definitions of oral health indicators and CVD outcomes. More important, a significant proportion of studies reported unclear definitions for CVD outcomes. The search strategy did not reveal any randomized controlled trials. Time-varying exposures, propensity methods, mediation analysis, and competing risks analysis are used infrequently in the identified studies. CONCLUSION: There is a need for a universally accepted conceptual framework on the association between oral health and CVD to derive more consistent definitions for oral health and CVD outcomes that are aligned with the investigated research questions. There is also a need to use emerging research methods to maximize the impact of research in this area. KNOWLEDGE TRANSFER STATEMENT: Clinical heterogeneity is evident in the definitions of oral health indicators and cardiovascular disease outcomes. Propensity methods, mediation analysis, and competing risks analysis are used infrequently in the identified studies. The identified clinical heterogeneity and methodological gaps interfere with summarizing existing evidence and understanding their practical implications. Advancing the current understanding of the associations between oral health and cardiovascular disease goes hand in hand with minimizing clinical heterogeneity and closing the identified methodological gaps.
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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 | 0.258 | 0.506 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| 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".