Comparison of Water Flosser and Interdental Brush on Plaque Removal: A Single-Use Pilot Study.
Bibliographic record
Abstract
OBJECTIVES: The purpose of this randomized, single-use, single-blind, two-group, parallel pilot study was to compare the plaque removal ability of a water flosser and interdental brush when used in combination with a manual toothbrush. METHODS: Twenty-eight (28) subjects completed the study. Subjects were randomly assigned to one of two groups: Waterpik®Water Flosser (WF) plus manual tooth brushing or interdental brushes (IDB) plus manual tooth brushing. Pre-cleaning plaque scores were obtained using the Rustogi Modification of the Navy Plaque Index (RMNPI). Subjects were instructed on the use of their interdental product. Post-cleaning scores were obtained after a supervised brushing and use of the interdental device. Scores were recorded for whole mouth, marginal, approximal, facial, and lingual regions for each subject. RESULTS: There were no differences between the groups for pre-cleaning plaque scores for whole mouth, marginal, approximal, facial, or lingual regions. Both groups showed significant reductions in plaque from baseline for all areas measured (p < 0.001). The WF group was significantly more effective than the IDB group for removing plaque from all areas measured. Specifically, the WF was 18% more effective for whole mouth and marginal areas, 20% for approximal areas, 11% for facial areas, and 29% for lingual areas. CONCLUSIONS: The Waterpik Water Flosser and manual toothbrush removes significantly more plaque from tooth surfaces (whole mouth, marginal, approximal, facial, and lingual) than interdental brushes and a manual toothbrush after a single use.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".