Personality Traits in Burning Mouth Syndrome Patients With and Without a History of Depression
Bibliographic record
Abstract
Objectives: So far, the strong link between neuroticism, chronic pain, and depression has been well-documented in literatures. Some suggested that they might share etiological factors, thus resulting in overlapping constructs. However, such effect has never been tested in burning mouth syndrome (BMS) patients, a complex phenomenon influenced by both neuropathic and psychopathological factors. We aim to clarify how personality affects individual's pain and pain-related experiences. Methods: Two hundred forty-eight patients with BMS provided demographic information and psychiatric history; completed Ten-Item Personality Inventory, a Visual Analog Scale of pain, and McGill Pain Questionnaire; and provided adequate parameters of depressive state, catastrophizing thinking, and central sensitization. Results: BMS patients with depression history suffered more severe clinical symptoms and scored higher in neuroticism and less in openness and extraversion than did those without psychiatric diagnoses. After age, sex, and duration of pain were controlled, neuroticism in BMS patients with depression correlates with affective dimension of pain. Instead, if psychiatric history is absent, neuroticism correlates with sensory dimension and pain intensity. In both groups, higher neuroticism, unlike other personality facets, contributed to a more severe clinical condition. Conclusion: Of the five traits, neuroticism appears to be the most crucial dimension associated with the pain symptoms and patient's conditions. This study implies that management of pain must extend beyond solely providing pain-relieving medication and must require a holistic and multidisciplinary approach.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".