Bacteremia following different oral procedures: Systematic review and meta‐analysis
Bibliographic record
Abstract
To evaluate the timing, duration and incidence of bacteremia following invasive dental procedures (IDPs) or activities of daily living (ADL). Eight databases were searched for randomized (RCTs) and nonrandomized controlled trials (nRCTs) evaluating bacteremia before and after IDPs or ADL in healthy individuals. The risk of bias was assessed by RoB 2.0 and ROBINS-I. For the meta-analysis, the primary outcomes were the timing and duration of bacteremia. The secondary outcome was the incidence of bacteremia, measuring the proportion of patients with bacteremia within 5 min after the end of the procedure compared with baseline. We included 64 nRCTs and 25 RCTs. Peak bacteremia occurred within 5 min after the procedure and then decreased over time. Dental extractions showed the highest incidence of bacteremia (62%-66%), followed by scaling and root planing (SRP) (44%-36%) and oral health procedures (OHP) (e.g., dental prophylaxis and dental probing without SRP) (27%-28%). Other ADL (flossing and chewing) (16%) and toothbrushing (8%-26%) resulted in bacteremia as well. The majority of studies had some concerns RCTs or moderate risk of bias nRCTs. Dental extractions, SRP and OHP, are associated with the highest frequency of bacteremia. Toothbrushing, flossing, and chewing also caused bacteremia in lower frequency.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".