Evaluating Burning Mouth Syndrome as a Comorbidity of Atypical Odontalgia: The Impact on Pain Experiences
Bibliographic record
Abstract
OBJECTIVE: This study aimed (1) to investigate the differences in clinical characteristics of patients between 2 groups, those who have atypical odontalgia (AO) only and those who have AO with burning mouth syndrome (BMS), and (2) to assess the influence of psychiatric comorbidity factors on patients' experiences. METHOD: Medical records and psychiatric referral forms of patients visiting the Psychosomatic Dentistry Clinic of Tokyo Medical and Dental University between 2013 and 2016 were reviewed. The final sample included 2 groups of 355 patients: those who have AO only (n = 272) and those who have AO with BMS (AO-BMS; n = 83). Clinicodemographic variables (gender, age, comorbid psychiatric disorders, and history of headache or sleep disturbances) and pain variables (duration of illness, pain intensity, and severity of accompanying depression) were collected. Initial pain assessment was done using the Short-Form McGill Pain Questionnaire, and depressive state was determined using the Zung Self-Rating Depression Scale. RESULTS: The average age, female ratio, and sleep disturbance prevalence in the AO-only group were significantly lower than those in AO-BMS group. AO-BMS patients rated overall pain score and present pain intensity significantly higher than did the AO-only patients (P = 0.033 and P = 0.034, respectively), emphasizing sharp (P = 0.049), hot-burning (P = 0.000), and splitting (P = 0.003) characteristics of pain. Patients having comorbid psychiatric disorders had a higher proportion of sleep disturbance in both groups and a higher proportion of depressive state in the AO-only group. CONCLUSIONS: AO-BMS patients have different epidemiological characteristics, sleep quality, and pain experiences compared to AO-only patients. The presence of psychiatric comorbidities in both groups may exacerbate sleep quality. We suggest that BMS as a comorbid oral disorder in AO patients contributes to a more intensively painful experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".