A critical review of protocols for conventional microwave oven use for denture disinfection.
Bibliographic record
Abstract
INTRODUCTION: The lack of proper denture hygiene may cause denture stomatitis and be detrimental to older adults' health. The cleaning of complete dentures should be quick, efficient, and easy to perform, although it might not guarantee disinfection. The use of a microwave oven has been suggested for aiding in the disinfection of complete dentures, but lacks a gold standard protocol. OBJECTIVES: To critically review the literature on protocols for complete denture disinfection using conventional microwave ovens. METHODS: A comprehensive literature search through PubMed Central, Cochrane Database of Systematic Reviews, and Ovid MEDLINE (R) In-Process focused on publications in English dealing with microwave therapy for denture disinfection, and on the protocols used. RESULTS: A total of 266 articles with the full-text available were found; 31 were included in this manuscript after 236 were excluded. The protocols for microwave oven use for disinfection of complete dentures varied in terms of oven potency, time used for microwaving, and solution in which the complete dentures were immersed. CONCLUSIONS: There is still no standardized protocol for microwave oven use for denture disinfection. Although underutilized in residential care, daily denture hygiene seems to still be the optimal method for controlling fungal infections and denture stomatitis.
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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 | 0.037 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".