Impact of dental visiting patterns on oral health: A systematic review of longitudinal studies
Bibliographic record
Abstract
AIM: To systematically review longitudinal studies investigating the impact of dental visiting patterns on oral health across the life course. METHODS: Five databases (MEDLINE, Embase, Scopus, Web of Science, CINAHL) were searched up to March 2023. Results were screened based on eligibility criteria in a two-stage process: title and abstract, and full-text review. A backward search of reference lists and a forward search of citations of the included papers was also conducted. The quality of the included papers was assessed using the Newcastle-Ottawa Scale. Key study information was extracted and a narrative synthesis of the findings was performed. RESULTS: Eleven papers from five longitudinal studies in five countries (Australia, Brazil, China, New Zealand, Sweden) met the inclusion criteria. Studies of moderate to high quality consistently reported that regular dental attendance was associated with having less dental caries experience, fewer missing teeth and better oral health-related quality of life. Inconsistent findings were observed for decayed teeth, and no association was found for periodontal condition. CONCLUSIONS: This review highlights an association between regular dental visiting pattern and improved oral health, notably less dental caries experience and better oral health-related quality of life. Dental attendance emerges as an important predictor of oral health across the life course, underscoring the importance of routine dental care. REGISTRATION INFORMATION: The PROSPERO registration number is CRD42023396380.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".