Psychosocial profiles of patients with burning mouth syndrome.
Bibliographic record
Abstract
AIMS: Burning mouth syndrome (BMS) is estimated to affect 1 to 5% of the adult population, with women experiencing symptoms more frequently than men. The purpose of this study was to examine the psychosocial profiles of BMS patients to determine whether psychologic factors are related to pain reports. Based on previous literature, it was hypothesized that patients with BMS would be characterized by clinical elevations on standardized psychologic assessment instruments that included the Revised Symptom Checklist (SCL-90R) and the Multidimensional Pain Inventory (MPI). METHODS: Thirty-three BMS patients completed the McGill Pain Questionnaire, MPI, and SCL-90R during their initial clinical evaluation session. The SCL-90R and MPI data were then summarized and presented in standardized format (T-scores) to enable meaningful comparisons with larger population samples that included both a chronic pain population and a normal nonclinical sample. RESULTS: The T-score for the overall pain severity on the MPI was 40.8 (SD 12.8). For the entire BMS sample, there was no evidence for significant clinical elevations on any of the SCL-90R subscales, including depression, anxiety, and somatization. Moreover, patients reported significantly fewer disruptions in normal activities as a result of their oral burning pain than did a large sample of chronic pain patients. CONCLUSION: These findings indicate that, as a group, this sample of BMS patients did not report significant psychologic distress. There were, however, individual cases (7 of 33, or 21%) where psychometric data indicated a likelihood of psychologic distress, and further evaluation by a competent health professional would be warranted for those individuals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".