Efficacy of water flossing on clinical parameters of inflammation and plaque: A 4‐week randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: The primary prevention of periodontitis is controlling gingivitis daily. The study objective was to compare the efficacy of a pulsating water flosser to a pulsating water flosser infused with air microbubbles on clinical signs of inflammation and plaque. METHODS: One hundred and five participants were enrolled in this single-blind, single-centre, parallel, 4-week, IRB/IEC-approved clinical trial. Participants were randomly assigned to one of three groups: water flosser (WF) plus manual toothbrush, water flosser infused with microbubbles of air (MBWF) plus manual toothbrush, or dental floss (DF) plus manual toothbrush. Bleeding on probing (BOP), Modified Gingival Index (MGI) and Rustogi Modification Navy Plaque Index (RMNPI) scores were recorded at baseline, 2 and 4 weeks. RESULTS: All participants completed the study (n = 105). All groups showed a statistically significant reduction for BOP, MGI and RMNPI at 4 weeks (p < 0.05, except DF marginal RMNPI). The WF group showed a statistically significant greater reduction in whole mouth BOP (0.41) compared to MBWF (0.32) and DF (0.19). This was also true for MGI (0.37, 0.30 and 0.20, respectively) and RMNPI (0.13, 0.11 and 0.06, respectively; p < 0.05 for all comparisons). No adverse events were reported. CONCLUSION: This study demonstrates that a manual toothbrush and water flosser, with or without microbubbles, is an effective oral care regimen for controlling gingivitis over 4 weeks.
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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) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".