Does computerized anaesthesia reduce pain during local anaesthesia in paediatric patients for dental treatment? A systematic review and meta-analysis.
Priscila de Camargo SmolarekLeticia Maira WambierLeonardo Siqueira SilvaAna Cláudia Rodrigues ChibinskiPublished in: International journal of paediatric dentistry (2019)
This systematic review and meta-analysis analysed whether pain and disruptive behaviour can be decreased by the use of computerized local dental anaesthesia (CDLA) in children. The literature was screened to select randomized clinical trials that compared computerized and conventional anaesthesia. The primary outcome was pain perception during anaesthesia; the secondary, disruptive behaviour. The risk of bias of individual papers and the quality of the evidence were evaluated. After search, 8389 records were found and 20 studies remained for the qualitative and quantitative syntheses. High heterogeneity was detected for both outcomes. For the pain perception, the overall analysis showed a standard mean difference of -0.78 (-1.31, -0.25) favouring CDLA; however, when only studies at low risk of bias were analysed (subgroup analysis), there was no difference between the two techniques [-0.12(-0.46, 0.22)]. For disruptive behaviour, no differences were detected for continuous [-0.26 (-0.68, 0.16)] or dichotomous data [0.81 (0.62, 1.06)]. The quality of evidence was judged as low for pain perception and very low for disruptive behaviour. It is concluded that there is no difference in the pain perception and disruptive behaviour in children subjected to computerized or conventional dental local anaesthesia. Notwithstanding, the quality of the available evidence is low.
Keyphrases
- chronic pain
- pain management
- neuropathic pain
- young adults
- end stage renal disease
- emergency department
- clinical decision support
- systematic review
- randomized controlled trial
- intensive care unit
- type diabetes
- chronic kidney disease
- clinical trial
- spinal cord injury
- ejection fraction
- adipose tissue
- mass spectrometry
- machine learning
- single cell
- weight loss
- deep learning
- big data
- postoperative pain
- smoking cessation