The effect of photobiomodulation therapy in common maxillofacial injuries: Current status
Bibliographic record
Abstract
The use of photobiomodulation therapy (PBMT) may be used for treating trauma to the maxillofacial region. The effects of PBMT on maxillofacial injuries were discussed in this review article. The electronic databases Pubmed, Scopus, and Web of Science were thoroughly searched. This review included in vitro, in vivo, and clinical studies describing how PBMT can be used in maxillofacial tissue engineering and regenerative medicine. Some studies suggest that PBMT may offer a promising therapy for traumatic maxillofacial injuries because it can stimulate the differentiation and proliferation of various cells, including dental pulp cells and mesenchymal stem cells, enhancing bone regeneration and osseointegration. PBMT reduces pain and swelling after oral surgery and tooth extraction in human and animal models of maxillofacial injuries. Patients with temporomandibular disorders also benefit from PBMT in terms of reduced inflammation and symptoms. PBMT still has some limitations, such as the need for standardizing parameters. PBMT must also be evaluated further in randomized controlled trials in various maxillofacial injuries. As a result, PBMT offers a safe and noninvasive treatment option for patients suffering from traumatic maxillofacial injuries. PBMT still requires further research to establish its efficacy in clinical practice and determine the optimal parameters.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".