Association between Acute Myocardial Infarction and Periodontitis: A Review of the Literature.
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD), particularly acute myocardial infarction (AMI), is the leading cause of death worldwide. In India, myocardial events are expected to be the fastest growing cause of death between 2005 and 2015. Thus, in order to prevent and manage the onset of the prevailing AMI epidemic, there is a crucial need to explore different dependent and independent risk factors of AMI, as well as its relationship with other systemic diseases and ill health conditions. One such possible relationship could be an association between AMI and periodontal diseases. OBJECTIVES AND METHODOLOGY: The aim of this study was to review the existing literature to assess the strength of association between AMI and periodontitis in the context of Indian, particularly North Indian, populations and to outline key knowledge gaps in this field. FINDINGS: Review of the literature clearly indicates that evidence on the association between periodontitis and AMI in Indian populations, as well as other populations worldwide, is limited. The number of studies done so far is relatively low. Further, inadequate sample size, retrospective data analyses, potential residual confounding factors, inconsistent definitions of exposure and outcome variables, and reported diversity in results, are some of the other key limitations. CONCLUSIONS: Insufficient evidence is available to justify that periodontal interventions can prevent the onset or progression of acute myocardial events. More longitudinal clinical trials and case-control studies with well controlled confounding factors and valid outcome and exposure measures are needed for determining the true association between the conditions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".