Psychiatric comorbidities in patients with Atypical Odontalgia
Bibliographic record
Abstract
OBJECTIVE: Atypical Odontalgia (AO) is a condition characterized by tooth pain with no apparent cause. Although psychiatric comorbidity seems to be very common, it has rarely been studied. To clarify the influence of psychiatric comorbidity on the clinical features in patients with AO, we retrospectively evaluated their examination records. METHODS: Clinical features and psychiatric diagnoses of 383 patients with AO were investigated by reviewing patients' medical records and referral letters. Psychiatric diagnoses were categorized according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). We also analyzed visual analogue scale (VAS), self-rating depression scale (SDS), and the short-form McGill pain questionnaire (SF-MPQ) scores. RESULTS: Of the 383 patients with AO, 177 (46.2%) had comorbid psychiatric disorders. The most common were depressive disorders (15.4%) and anxiety disorders (10.1%). Serious psychotic disorders such as bipolar disorder (3.0%) and schizophrenia (1.8%) were rare. Dental trigger of AO was reported in 217 (56.7%) patients. There were no significant correlations between psychiatric comorbidities and most of the demographic features. Higher VAS and SDS scores, higher frequency of sleep disturbance, and higher ratings of "Fearful" and "Punishing-cruel" descriptors of the SF-MPQ were found in patients with psychiatric comorbidity. CONCLUSIONS: About half of AO patients had comorbid psychiatric disorders. Dental procedures are not necessarily causative factors of AO. In AO patients with comorbid psychiatric disorders, pain might have a larger emotional component than a sensory one. VAS, SDS, and SF-MPQ scores might aid in the noticing of underlying comorbid psychiatric disorders in AO patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".