Prevalence of Surgical Site Infections Following Coronectomy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: This systematic review and meta-analysis aimed to investigate the prevalence of surgical site infections (SSIs) following coronectomy of mandibular third molars. Methods: A comprehensive literature search was conducted in Medline, Scopus, Web of Science, and Google Scholar databases up to 30 July 2024. Two independent reviewers performed study selection, data extraction, and quality assessment using the Newcastle–Ottawa Scale. Observational studies assessing SSI prevalence following coronectomy were included. The pooled prevalence of SSI with 95% confidence intervals (CI) was calculated using a random-effects model. Heterogeneity was assessed using the I2 statistic, and meta-regression was conducted to explore the influence of continuous variables. Results: A total of 22 studies involving 2173 coronectomy procedures were included. The overall pooled prevalence of SSI was 2.4% (95% CI: 1–4.3%), with substantial heterogeneity (I2 = 81%). Meta-regression showed no significant effect of the examined variables on SSI prevalence. No study was identified as a significant outlier. Quality assessments revealed that all studies had moderate methodological quality. Conclusions: Considerable heterogeneity was observed, likely due to variations in study settings, geographical regions, and timeframes, among other factors. Therefore, this study underscores the need for further rigorous research to better understand SSI risk factors and enhance management strategies for this postoperative complication.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".