FP435ORAL MUCOSAL LESIONS AND ASSOCIATION WITH MORTALITY IN HEMODIALYSIS PATIENTS: A PROSPECTIVE COHORT ANALYSIS (ORAL-D SUBSTUDY)
Bibliographic record
Abstract
INTRODUCTION: Impaired oral health is prevalent and frequently severe among adults treated with long-term hemodialysis. We evaluate the prevalence of oral mucosal lesions and association with total and cardiovascular mortality among hemodialysis patients. METHODS: We did a planned analysis of ORAL-D. ORAL-D is a prospective multinational cohort study evaluating a standardized oral and dental examination among 4726 hemodialysis. Oral mucosal lesions included ulceration, red lesion, white lesion, geographical tongue, fissured tongue, candidiasis and herpes per WHO guidelines. The association between mucosal lesions and all-cause and cardiovascular mortality was estimated using a Cox proportional hazard regression model adjusted for age, sex, education, smoking history, prior myocardial infarction, diabetes, hemoglobin, serum albumin, serum phosphorus, time on dialysis and body mass index, and clustered by country. The outcomes were prevalence and all-cause and cardiovascular mortality. RESULTS: 4205 adults (mean age 61.6 ± 15.6 years) had a complete oral examination. 40% had at least 1 mucosal lesion. The point prevalence of oral lesions was (in ascending order of frequency): oral herpes 0.5%, mucosal ulceration 1.7%, neoformation 2.0%, white lesion 3.5%, red lesion 4.0%, oral candidiasis 4.6%, geographical tongue 4.9%, petechial lesions 7.9%, and fissured tongue 10.7%. During median follow-up of 3.5 years, 2114 patients died (1013 from cardiovascular causes). Oral candidiasis was associated with all-cause mortality (adjusted hazard ratio (aHR) 1.37, 95% CI 1.00 to 1.86) and cardiovascular mortality (aHR 1.64, 95% CI 1.09 to 2.46). There was no association observed for any other oral mucosal lesion with mortality. CONCLUSIONS: Oral mucosal lesions are prevalent in hemodialysis patients. Oral candidiasis appears to be a risk factor for death. Marinella Ruospo, Suetonia C Palmer, Giusi Graziano, Patrizia Natale, Valeria Saglimbene, Massimo Petruzzi, Michele De Benedittis, Jonathan C Craig, David W Johnson, Pauline Ford, Marcello Tonelli, Eduardo Celia, Ruben Gelfman, Miguel R Leal, Marietta Török, Paul Stroumza, Luc Frantzen, Anna Bednarek-Skublewska, Jan Dulawa, Domingo del Castillo, Staffan Schön, Amparo G Bernat, Jörgen Hegbrant, Charlotta Wollheim, Letizia Gargano, Giovanni FM Strippoli on behalf of the ORAL-D Investigatorson behalf of the ORAL-D Investigators
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".