Risk of complications among diabetics self-reporting oral health status in Canada: A population-based cohort study
Bibliographic record
Abstract
Abstract Background Periodontitis has persistently been associated with diabetes and poor health outcomes. While clear associations have been identified for the diabetes–oral health link, less is known about the implications of poor oral health on incident complications of diabetes. This study sought to investigate the risk of diabetes complications associated with self-reported “poor to fair” and “good to excellent” oral health status among diabetics living in Ontario, Canada. Methods This cohort study was undertaken of diabetics from the Canadian Community Health Survey (2003 and 2007-8). Self-reported oral health was linked to electronic health records at the Institute for Clinical Evaluative Sciences. Participants under the age of 40, missing self-reported oral health and those who could not be identified in linked databases were excluded (N=5,183). A series of Cox Proportional hazard models were constructed to determine the risk of diabetes complications. Participants who did not experience any diabetes complication were censored at time of death or at the study termination date (March 31, 2016). Models were adjusted for age and sex, followed by social characteristics and behavioural factors. Results Diabetes complications differed by self-reported oral health. For those reporting “poor to fair” oral health, the hazard of a diabetes complication was 30% greater (HR 1.29 95%CI 1.03, 1.61) than those reporting “good to excellent” oral health. Conclusions Our findings indicate that oral health status is associated with increased risk for complications among diabetics, after adjusting for a wide range of confounders. Examining oral health and the risk for diabetes complications from a broader perspective including socio-behavioural and biological pathways is principal for informing policies and interventions that aim to mitigate the burdens of poor systemic health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".