Effects of photobiomodulation with low‐level laser therapy in burning mouth syndrome: A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Photobiomodulation (PBM) has proven to be effective in different painful conditions. OBJECTIVES: To assess the effect of photobiomodulation for pain management in burning mouth syndrome (BMS) patients, besides analysing the impact on different aspects of quality of life. METHODS: during 10 sessions, comparing with a placebo group (n = 10) with the laser turned off. Pain was assessed using the visual analogue scale (VAS) before starting each treatment session, and at the 1-month and 4-month follow-up appointments. Some validated questionnaires for general health were also complete: SF-36, OHIP-14, Epworth, SCL 90-R and McGill. RESULTS: All patients (n = 10) in the study group improved their pain ending treatment and remaining among 90% (n = 9) in the 4-month follow-up. Significant improvement was found in the study group in some sections of McGill questionnaire, Epworth scale, and SCL 90-R at the end of the treatment and in the 1-month and 4-month follow-ups. CONCLUSIONS: Photobiomodulation seems to be effective in reducing pain in patients with BMS, as well as, having a positive impact on the psychological state of these patients.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".