Interventions for the Management of Denture Stomatitis: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVES: To assess the effectiveness of different interventions for treating or preventing denture stomatitis (DS). DESIGN: Systematic review. SETTING: Randomized controlled trials (RCTs) comparing any agent or procedure prescribed to treat or prevent DS in adults. PARTICIPANTS: Older adults with denture stomatitis. MEASUREMENTS: There were two main outcomes reported in the trials included in this review: clinical signs of DS and remaining presence of yeast. There were no restrictions regarding language or date of publication. The search period was up to February 2016. RESULTS: Thirty-five studies were included in the systematic review, with 32 judged as having high risk of bias. Three RCTs compared nystatin with placebo and found a significant effect on the reduction of clinical signs of stomatitis (risk ratio (RR) = 0.51, 95% confidence interval (CI) = 0.36-0.72), four RCTs compared nystatin with placebo and found a significant effect on mycological assessment (RR = 0.61, 95% CI = 0.46-0.80). Five studies of disinfectant agents also showed a significant effect in comparison with an inactive agent (RR = 0.52, 95% CI = 0.30-0.92) in clinical assessment. No evidence was found of an effect of miconazole, amphotericin, or imidazolic drugs. No RCT evaluated the effectiveness of preventive approaches. CONCLUSION: The results are supportive of the use of nystatin and disinfecting agents in the treatment of DS, but clinicians need to be aware that individual studies had high risk of bias and that the overall quality of the individual reports was judged to be low.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.030 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".