Patterns of oral disease in adults with chronic kidney disease treated with hemodialysis
Bibliographic record
Abstract
BACKGROUND: Oral disease is a potentially treatable determinant of mortality and quality of life. No comprehensive multinational study to quantify oral disease burden and to identify candidate preventative strategies has been performed in the dialysis setting. METHODS: The ORAL disease in hemoDialysis (ORALD) study was a prospective study in adults treated with hemodialysis in Europe (France, Hungary, Italy, Poland, Portugal and Spain) and Argentina. Oral disease was assessed using standardized WHO methods. Participants self-reported oral health practices and symptoms. Sociodemographic and clinical factors associated with oral diseases were determined and assessed within nation states. RESULTS: Of 4726 eligible adults, 4205 (88.9%) participated. Overall, 20.6% were edentulous [95% confidence interval (CI), 19.4-21.8]. Participants had on average 22 (95% CI 21.7-22.2) decayed, missing or filled teeth, while moderate to severe periodontitis affected 40.6% (95% CI 38.9-42.3). Oral disease patterns varied markedly across countries, independent of participant demographics, comorbidity and health practices. Participants in Spain, Poland, Italy and Hungary had the highest mean adjusted odds of edentulousness (2.31, 1.90, 1.90 and 1.54, respectively), while those in Poland, Hungary, Spain and Argentina had the highest odds of ≥14 decayed, missing or filled teeth (23.2, 12.5, 8.14 and 5.23, respectively). Compared with Argentina, adjusted odds ratios for periodontitis were 58.8, 58.3, 27.7, 12.1 and 6.30 for Portugal, Italy, Hungary, France and Poland, respectively. National levels of tobacco consumption, diabetes and child poverty were associated with edentulousness within countries. CONCLUSIONS: Oral disease in adults on hemodialysis is very common, frequently severe and highly variable among countries, with much of the variability unexplained by participant characteristics or healthcare. Given the national variation and high burden of disease, strategies to improve oral health in hemodialysis patients will require implementation at a country level rather than at the level of individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".