The association between adverse childhood experiences and oral health: A systematic review
Bibliographic record
Abstract
OBJECTIVE: It is well established that adverse childhood experiences (ACEs) negatively affect health and are associated with health-risk behaviors. This study aimed to provide a systematic review of the studies that examine the relationship between ACE exposure and oral health among adults aged 18 years and older. METHODS: The following electronic databases were searched in January 2022: MEDLINE, Cochrane, Web of Science, CINAHL via EBSCOhost, ProQuest, ScienceDirect, and Google Scholar. Data were extracted independently by two reviewers. The quality of the studies was assessed using the Newcastle-Ottawa Scale. RESULTS: Among the 292 articles identified, four met the eligibility criteria. All included studies were cross-sectional and of satisfactory quality. The dental outcomes included: last dental visit, last dental cleaning, number of filled teeth, number of extracted teeth, and number of remaining teeth. The studies showed that exposure to ACE was negatively associated with oral health. The relationship between ACE score and oral health outcome measures was found to be directly proportional. CONCLUSION: There is an association between ACE and poor oral health. Moreover, the association was proven to have a dose-response relationship. Given that the studies in the literature were cross-sectional, causality cannot be determined with certainty, therefore interpretation of the results should be cautious. Longitudinal follow-up studies are needed to understand how ACEs contribute to oral diseases later in life.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".