Does oral health influence school performance and school attendance? A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: To examine the evidence on the influence of oral health status on school performance and school attendance in children and adolescents. DESIGN: A systematic review was performed in accordance with PRISMA included epidemiological studies that assessed concomitantly oral health measures, participants' school performance and/or school attendance. Electronic search was conducted on MEDLINE, SCOPUS, Web of Science, ScienceDirect, and LILACS. Studies published up to May 2018 in any language were eligible. The risk of bias was assessed using the Newcastle-Ottawa Scale. Meta-analysis was used to obtain pooled estimates between oral health measures and school performance and school attendance. RESULTS: Eighteen studies were included. Of them, fifteen studies were used for the meta-analyses. Most studies were assessed as moderate quality. Children with one or more decayed teeth had higher probability of poor school performance (OR = 1.44 95%CI: 1.24-1.64) and poor school attendance (OR = 1.57 95%CI: 1.08-2.05) than caries-free children. Poor parent's perception of child's oral health increased the odds of worse school performance (OR = 1.51 95%CI: 1.10-1.92) and poor school attendance (OR = 1.35 95%CI: 1.14-1.57). CONCLUSIONS: Children and adolescents with dental caries and those reporting worse oral health experience poor school performance and poor school attendance.
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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".