Oral Health-Related Quality of Life, A Proxy of Poor Outcomes in Patients on Peritoneal Dialysis
Bibliographic record
Abstract
Introduction: We sought to evaluate the associations of poor oral health hygiene with clinical outcomes in patients receiving peritoneal dialysis (PD). Methods: As part of the multinational Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS), PD patients from 22 participating PD centers throughout Thailand were enrolled from May 2016 to December 2019. The data were obtained from questionnaires that formed part of the PDOPPS. Oral health-related quality of life (HRQoL) used in this study was the short form of the oral health impact profile (oral health impact profile [OHIP]-14, including 7 facets and 14 items). Patient outcomes were assessed by Kaplan-Meier analysis. Cox proportional hazards model regression was used to estimate associations between oral HRQoL and clinical outcomes. Results: Of 5090 PD participants, 675 were randomly selected, provided informed consent, and completely responded to the OHIP-14 questionnaire. The median follow-up time of the study was 3.5 (interquartile range = 2.7-5.1 months) years. Poor oral health was associated with lower educational levels, diabetes, older age, marriage, and worse nutritional indicators (including lower time-averaged serum albumin and phosphate concentrations). After adjusting for age, sex, comorbidities, serum albumin, shared frailty by study sites, and PD vintage, poor oral health was associated with increased risks of peritonitis (adjusted hazard ratio [HR] = 1.45, 95% confidence interval [CI]: 1.06-2.00) and all-cause mortality (adjusted HR = 1.55, 95% CI: 1.04-2.32) but not hemodialysis (HD) transfer (adjusted HR = 1.89, 95% CI: 0.87-4.10) compared to participants with good oral health. Conclusion: Poor oral health status was present in one-fourth of PD patients and was independently associated with a higher risk of peritonitis and death.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".