Dental Health and Mortality in People With End-Stage Kidney Disease Treated With Hemodialysis: A Multinational Cohort Study
Bibliographic record
Abstract
BACKGROUND: Dental disease is more extensive in adults with chronic kidney disease, but whether dental health and behaviors are associated with survival in the setting of hemodialysis is unknown. STUDY DESIGN: Prospective multinational cohort. SETTING & PARTICIPANTS: 4,205 adults treated with long-term hemodialysis, 2010 to 2012 (Oral Diseases in Hemodialysis [ORAL-D] Study). PREDICTORS: Dental health as assessed by a standardized dental examination using World Health Organization guidelines and personal oral care, including edentulousness; decayed, missing, and filled teeth index; teeth brushing and flossing; and dental health consultation. OUTCOMES: All-cause and cardiovascular mortality at 12 months after dental assessment. MEASUREMENTS: Multivariable-adjusted Cox proportional hazards regression models fitted with shared frailty to account for clustering of mortality risk within countries. RESULTS: During a mean follow-up of 22.1 months, 942 deaths occurred, including 477 cardiovascular deaths. Edentulousness (adjusted HR, 1.29; 95% CI, 1.10-1.51) and decayed, missing, or filled teeth score ≥ 14 (adjusted HR, 1.70; 95% CI, 1.33-2.17) were associated with early all-cause mortality, while dental flossing, using mouthwash, brushing teeth daily, spending at least 2 minutes on oral hygiene daily, changing a toothbrush at least every 3 months, and visiting a dentist within the past 6 months (adjusted HRs of 0.52 [95% CI, 0.32-0.85], 0.79 [95% CI, 0.64-0.97], 0.76 [95% CI, 0.58-0.99], 0.84 [95% CI, 0.71-0.99], 0.79 [95% CI, 0.65-0.95], and 0.79 [95% CI, 0.65-0.96], respectively) were associated with better survival. Results for cardiovascular mortality were similar. LIMITATIONS: Convenience sample of clinics. CONCLUSIONS: In adults treated with hemodialysis, poorer dental health was associated with early death, whereas preventive dental health practices were associated with longer survival.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".