Assessment of the pre-emptive effect of photobiomodulation in the postoperative period of impacted lower third molar extractions: A randomized, controlled, double-blind study protocol.
Daniel Rodríguez SalaberryMaría Laura Laura Hermida BrunoRolf Wilhem Consolandich CirisolaPriscila Larcher LongoMaria Cristina ChavantesRicardo Scarparo NavarroMarcela Letícia Leal GonçalvesAna Paula Taboada SobralThais GimenezCinthya Cosme Gutierrez DuranLara Jansiski MottaSandra Kalil BussadoriAnna Carolina Ratto Tempestini HorlianaRaquel Agnelli Mesquita FerrariKristianne Porta Santos FernandesPublished in: PloS one (2024)
Photobiomodulation is a safe option for controlling pain, edema, and trismus when applied postoperatively in third molar surgery. However, administration prior to surgery has been under-explored. This study aims to explore the effectiveness of pre-emptive photobiomodulation in reducing postoperative edema in impacted lower third molar extractions. Two groups of healthy individuals undergoing tooth extraction will be randomly assigned: Control group receiving pre-emptive corticosteroid and simulated photobiomodulation, and Photobiomodulation Group receiving intraoral low-intensity laser and extraoral LED cluster application. The primary outcome will be postoperative edema after 48 h. The secondary outcomes will be pain, trismus dysphagia, and analgesic intake (paracetamol). These outcomes will be assessed at baseline as well as two and seven days after surgery. Adverse effects will be recorded. Data will be presented as means ± SD and a p-value < 0.05 will be indicative of statistical significance.
Keyphrases
- wound healing
- patients undergoing
- minimally invasive
- neuropathic pain
- study protocol
- chronic pain
- randomized controlled trial
- coronary artery bypass
- clinical trial
- pain management
- double blind
- systematic review
- placebo controlled
- type diabetes
- spinal cord
- metabolic syndrome
- open label
- big data
- high speed
- spinal cord injury
- artificial intelligence
- anti inflammatory
- weight gain
- data analysis