Stress, allostatic load, and periodontal diseases
Bibliographic record
Abstract
Psychosocial stress plays an important role in periodontal disease through biological and behavioral pathways. In this paper we review studies that examine the relationship between stress and periodontal diseases, and discuss the different measures used to assess stress. Self-reported measures, such as the Perceived Stress Scale and the Stress Appraisal Measure, have traditionally been used to assess stress. Frequent and repeated exposure to stressor(s) leads to wear and tear of the body's systems, resulting in what is known as allostatic load. In recent years, few studies examining the relationship between stress and periodontal diseases have used an aggregate variable, including primary and secondary markers of allostatic load, as a biological marker of stress. While research on the relationship between allostatic load and periodontal disease is still developing, as most of the studies used cross-sectional data, this line of research presents a good opportunity for establishing a composite biological indicator as a risk factor for periodontal disease. Such an indicator is also potentially beneficial for personalized periodontics as it will help to target intervention to specific levels of risk and will help in integrating oral and general health promotion policies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".