The Effects of Tooth Brushing on Whole Salivary Flow Rate in Older Adults
Bibliographic record
Abstract
Objectives . (1) To determine whether manual (MTB), or electric, tooth brushing (ETB) modulates whole salivary flow rate in older adults who are free of systemic disease. (2) To determine the duration of the brushing-related modulation of salivary flow rate. (3) To compare salivary flow rate modulation associated with MTB and ETB. Method . Twenty-one adults aged 60 years and older participated in two experimental sessions during which they used a manual, or electric, toothbrush to brush their teeth, tongue, and palate. Whole salivary flow rates were determined using the draining method before, during, and after brushing. Differences in salivary flow rates across time periods, and between conditions, were examined using paired samples<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:math>-tests applying a Holm-Bonferroni sequential procedure (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:msub><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">c</mml:mi><mml:mi mathvariant="normal">o</mml:mi><mml:mi mathvariant="normal">r</mml:mi><mml:mi mathvariant="normal">r</mml:mi></mml:mrow></mml:msub><mml:mo><</mml:mo><mml:mn fontstyle="italic">0.0045</mml:mn></mml:math>). The relationship between tooth brushing and age with respect to maximum salivary flow rate increase was examined using Pearson’s correlation coefficient (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn fontstyle="italic">0.05</mml:mn></mml:math>). Results/Conclusion . Whole salivary flow rates increased during, and for up to 5 minutes following, tooth brushing in adults aged 60 years and older who were free of systemic disease. The salivary effects of MTB and ETB were not significantly different. A moderate, positive correlation was observed between tooth-brushing-related maximum salivary flow rate increase and age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".