Conscious sedation for the management of dental anxiety in third molar extraction surgery: a systematic review
Bibliographic record
Abstract
BACKGROUND: Dental anxiety is a condition associated with avoidance of dental treatment and increased medical and surgical risks. This systematic review aims to summarize available evidence on conscious sedation techniques used for the management of Dental anxiety in patients scheduled for third molar extraction surgery, to identify best approaches and knowledge gaps. METHODS: A comprehensive search was conducted including MEDLINE/Pubmed, EMBASE, SCOPUS, clinicaltrials.gov and the Cochrane Database of Systematic Reviews through March 2019. Only randomized controlled trials were included. PRISMA guidelines were followed. Risk of bias was appraised as reported in the Cochrane Handbook for Systematic Reviews of Interventions. RESULTS: Seventeen RCTs with a total of 1788 patients were included. Some aspects limited the feasibility of a meaningful meta-analysis, thus a narrative synthesis was conducted. Conscious sedation was associated with improvement in Dental anxiety in six studies. One study reported lower cortisol levels with midazolam vs. placebo, while another study found significant variation in perioperative renin levels with remifentanil vs. placebo. CONCLUSIONS: This review found inconclusive and conflicting findings about the role of Conscious sedation in managing Dental anxiety during third molar extraction surgery. Relevant questions remain unanswered due to the lack of consistent, standardized outcome measures. Future research may benefit from addressing these limitations in study design.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".