Analgesics for Dental Implants: A Systematic Review
Bibliographic record
Abstract
Postsurgical pain is commonly associated with dental and oral surgery, and the use of analgesics has been investigated in the management of postoperative pain. This systematic review summarizes available evidence on analgesics used to manage dental implant surgery postoperative pain, to identify best therapeutic protocols and knowledge gap. A comprehensive search was conducted including MEDLINE/Pubmed, EMBASE, SCOPUS, clinicaltrials.gov, and the Cochrane Database of Systematic Reviews through May 2020. Only randomized controlled trials were included. PRISMA guidelines were followed, and risk of bias was appraised using Cochrane RoB2 tool. Eleven trials (762 patients overall) were included. Some aspects limited the feasibility of a meaningful meta-analysis; thus, a narrative synthesis was conducted. Risk of bias was low in four studies and high in two studies, while five studies raised some concerns due to the randomization process. Analgesic use seemed to be associated with improved postoperative outcomes (pain, patient's satisfaction, and need for rescue medication) when compared to placebo. Overall, this review suggests that the administration of analgesics may provide some advantages in the management of postoperative outcomes after dental implant placement, while indications about the best analgesics cannot be provided.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.006 | 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".