Association de l'exposition aux écrans avec la fréquence de brossage des dents et la santé bucco-dentaire : une revue systématique
Bibliographic record
Abstract
Introduction: this systematic review aimed to investigate the association of screen exposure with toothbrushing frequency, dental caries and periodontal diseases. Methods: PubMed, Scopus and Web of Science databases were searched on January 26th, 2023. Two reviewers, blind to each other, selected observational studies that investigated the association between screen time or screen addiction and the toothbrushing frequency, the caries experience, or the presence of gingivitis or periodontitis. They independently assessed the risk of bias with the Newcastle-Ottawa Scale. Results: from 1,193 potentially eligible records, 8 cross-sectional studies were included, conducted mainly in East Asia and in young people. Greater screen exposure was significantly associated with lower adherence to toothbrushing (4 studies out of 4), and higher caries experience (3 studies out of 5) and presence of periodontal diseases (2 studies out of 3). Half of the included studies were of good quality. Conclusion: this review highlights a potential relationship between greater exposure to screens and poorer oral health, although longitudinal studies are still required in this field. Public health policies and programs should address the global problem of the increasing use of screens to limit its consequences on oral and general health. Additionally, dentists should consider that patients who use screens intensively might be at high risk for oral diseases.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".