Evaluation of the Addition of a Water Flosser to Manual Brushing on Gingival Health.
Bibliographic record
Abstract
OBJECTIVES: The purpose of this clinical trial was to compare the use of a manual toothbrush plus water flosser to a manual toothbrush alone on clinical signs of inflammation. METHODS: Seventy-two subjects were randomized into two groups and completed this four-week, single-blind, parallel, clinical trial. Group 1 used a Waterpik®Water Flosser (WF) once daily and brushed with a manual toothbrush twice a day. Group 2 brushed twice a day with a manual toothbrush only (MT). Subjects in group 1 were provided written and verbal instructions for the water flosser and all participants used the toothpaste and manual brush provided, brushing as they normally do. Data were evaluated at baseline (BSL), two weeks (W2), and four weeks (W4) for bleeding on probing (BOP), Modified Gingival Index (MGI), and Rustogi Modification of the Navy Plaque Index (RMNPI). RESULTS: Both groups showed a significant reduction from BSL for BOP, MGI, and RMNPI at W2 and W4, except for MT W2 facial proximal MGI (p = 0.153) and marginal RMNPI (p = 0.324). The WF was significantly more effective than the MT for reducing BOP, MGI, and RMNPI at W2 and W4 for all areas measured. The WF was 3.13 times as effective for reducing BOP, 2.69 times for MGI, and 2.44 times for RMNPI at W4 (p < 0.001) for whole mouth scores. CONCLUSIONS: The addition of the Waterpik®Water Flosser to manual tooth brushing is significantly more effective for improving gingival health than manual tooth brushing alone.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".