Biopsychosocial factors in oral and systemic diseases: a scoping review
Bibliographic record
Abstract
Background: The association between chronic oral diseases and other major systemic health conditions, commonly referred to as the oral-systemic health connection, has been previously studied with several underlying common risk factors and pathways linking both groups of diseases. Psychosocial factors contribute to an increased susceptibility to chronic oral and non-oral diseases. The aim of this review is to summarize the current state of knowledge on the role of psychosocial stress in chronic oral and systemic diseases. Methods: A search strategy was built and a literature search was conducted using four databases (CINAHL, Embase, Medline, PsycINFO). A combination of search terms related to psychosocial stress, systemic disease, and oral conditions were used. Studies were eligible for inclusion if they included human adults (aged 18 years and older), included psychosocial factors as an exposure measure, and outcome measures of both an oral and systemic condition. Only English-language articles were considered. Pilot testing of the data extraction form and calibration were conducted and data were extracted independently by one researcher. Results: A total of fifteen articles out of eighty full-text articles screened were determined to be eligible for inclusion in this review. Periodontal disease was the most commonly studied oral disease, measured in 53% of included articles, with the most commonly studied systemic diseases being of mental health conditions (40%) and diabetes (47%). Psychosocial stress was measured using a range of psychometric indicators and/or biomarkers, including perceived stress, individual behaviours, childhood adversity, and cortisol. In total, fourteen studies found a positive association between measures of psychosocial stress and oral-systemic health. Conclusion: Psychosocial stress may be a common contributor to both chronic oral and non-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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".