Efficacy of sonic versus manual toothbrushing after professional mechanical plaque removal: A 6‐month randomized clinical trial
Bibliographic record
Abstract
AIM: The aim of this study was to compare the efficacy of two brushing methods (manual vs. sonic) in terms of plaque control after a session of professional mechanical plaque removal (PMPR). METHODS: Subjects with gingivitis underwent a session of PMPR and were randomly assigned to sonic (SB) or manual brushing (MB). Oral hygiene instructions were provided at baseline (BL), 2 (T0a), 4 (T0b) and 6 weeks (T1) and 6 months (T2). Plaque Index (PI), Gingival Index (GI) and bleeding on probing (BoP) were measured at BL, T1 and T2. The proportion of sites with PI, GI and BoP was modelled at site level using a negative binomial regression fitted via generalized linear mixed model accounting for intra-patient correlation. RESULTS: Thirty-two subjects were selected, 16 assigned to each group and 31 completed the study. PI, BoP and GI were comparable at BL. At T1, PI was successfully maintained at 6.21% for SB and 22.81% for MB, while at T2 reached 11.34% for SB and 28% for MB, favouring the SB group (p < 0.001). GI and BoP were significantly lower in the SB group at T1, with a BoP reduction for SB about 3 times higher than MB (p < 0.001). These parameters then levelled at T2 between the groups, with BOP reaching 0.14% versus 0.05% (p = 0.356) and GI 1.75% versus 3.52% (p = 0.020). CONCLUSION: Sonic brushing seemed to maintain a lower PI score compared to a manual brush at 6 months. BoP and GI resulted comparable.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".