Acute and chronic diabetes complications associated with self-reported oral health: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Oral health is associated with diabetes, but the chances of experiencing acute or chronic diabetes complications as per this association is unknown in Canada's most populous province, Ontario. This study assesses the impact of self-reported oral health on the likelihood of experiencing acute and chronic complications among a cohort of previously diagnosed diabetics. METHODS: A retrospective cohort study was conducted of diabetics (n = 5183) who participated in the Canadian Community Health Survey 2003 and 2007-08. Self-reported oral health status was linked to health encounters in electronic medical records until March 31, 2016. Multinomial regression models determined the odds of the first acute or chronic complication after self-report of oral health status. RESULTS: Thirty-eight percent of diabetics reporting "poor to fair" oral health experienced a diabetes complication, in comparison to 34% of those reporting "good to excellent" oral health. The odds of an acute or chronic complication among participants reporting "poor to fair" oral health status was 10% (OR 1.10; 95% CI 0.81, 1.51) and 34% (OR 1.34; 95% CI 1.11, 1.61) greater respectively, than among participants experiencing no complications and reporting "good to excellent" oral health. CONCLUSION: Self-reporting "poor to fair" oral health status is associated with a greater likelihood of chronic complications than acute complications. Further research regarding the underlying causal mechanisms linking oral health and diabetes complications is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".