Burning mouth syndrome and oral health-related quality of life: is there a change over time?
Bibliographic record
Abstract
BACKGROUND: The symptoms associated with burning mouth syndrome can be quite varied and can interfere with the every day lives of patients. Management of the condition can be challenging for clinicians. AIMS: To determine the oral health-related quality of life (OHRQOL) implications of BMS on patients over a period of time whilst undergoing treatment and to evaluate whether treatment interventions had a positive effect on OHRQOL. MATERIALS AND METHODS: Thirty-two individuals (26 females, 6 males, mean age 61 years, range 38-83 years) were enrolled in this study. Individuals were interviewed using Short-Form McGill Pain Questionnaire (SFMPQ), Visual Analogue Scale (VAS), the Hospital Anxiety and Depression Scale (HADS) and the Oral Health Impact Profile (OHIP-14), at weeks 0, 8 and 16. RESULTS: Scores from all outcome measures used decreased over the 16 weeks of the study. Statistically significant differences were found between time points for VAS pain scores (P < 0.001), HADS depression scores (P = 0.029), SFMPQ sensory pain scores (P < 0.01) and total scores for OHIP-14 (P < 0.05). CONCLUSION: Burning mouth syndrome has a negative impact on OHRQOL; however, individually tailored management of the condition can result in an improvement in patient-reported outcome measures including quality of life.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".