Periodontitis and early mortality among adults treated with hemodialysis: a multinational propensity-matched cohort study
Bibliographic record
Abstract
BACKGROUND: Periodontitis is associated with cardiovascular mortality in the general population and adults with chronic diseases. However, it is unclear whether periodontitis predicts survival in the setting of kidney failure. METHODS: ORAL-D was a propensity matched analysis in 3338 dentate adults with end-stage kidney disease treated in a hemodialysis network in Europe and South America designed to examine the association between periodontitis and all-cause and cardiovascular-related mortality in people on long-term hemodialysis. Participants were matched 1:1 on their propensity score for moderate to severe periodontitis assessed using the World Health Organization Community Periodontal Index. A random-effects Cox proportional hazards model was fitted with shared frailty to account for clustering of mortality risk within countries. RESULTS: Among the 3338 dentate participants, 1355 (40.6%) had moderate to severe periodontitis at baseline. After using propensity score methods to generate a matched cohort of participants with periodontitis similar to those with none or mild periodontal disease, moderate to severe periodontitis was associated with a lower risk of all-cause (9.1 versus 13.0 per 100 person years, hazard ratio 0.74, 95% confidence interval 0.61 to 0.90) and cardiovascular (4.3 versus 6.9 per 100 person years, hazard ratio 0.67, 0.51 to 0.88) mortality. These associations were not changed substantially when participants were limited to those with 12 or more natural teeth and when accounting for competing causes of cardiovascular death. CONCLUSION: In contrast to the general population, periodontitis does not appear to be associated with an increased risk of early death in adults treated with hemodialysis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".