Preoperative dental assessment for the reduction of periprosthetic joint infections in patients undergoing total joint replacement: a systematic review and meta-analysis
Bibliographic record
Abstract
The requirement for preoperative dental assessment (PDA) to prevent periprosthetic joint infection (PJI) after total joint arthroplasty (TJA) seems to be a common practice at least in some orthopaedic centres. There are few studies which have examined this intervention. Routine referral of patients for routine PDA increases costs and potentially prolongs the time to the procedure. In order to investigate the effect of PDA on the frequency of PJI after TJA, we conducted a systematic review with meta-analysis of observational studies including adult patients undergoing TJA. The search for eligible studies was performed across MEDLINE, EMBASE, Web of Science, and Google Scholar databases. The intervention group consisted of patients who had undergone PDA, while the control group consisted of patients without PDA. The main outcome was the presence of PJI. In addition to traditional meta-analysis, a Bayesian analysis and trial sequential analysis were performed. The analysis included five observational studies. Considering PJI as an outcome, the total risk of bias was assessed as serious. A total of 23 175 patients were included in those studies, of whom 12 324 had a PDA. There was no effect of PDA versus no PDA on the incidence of PJI (OR 0.86, 95% CI: 0.50-1.49; I² = 42%). Bayesian analysis showed that the posterior probability of PDA reducing the frequency of PJI was 69.1%. Thus it was concluded that, in patients undergoing TJA, it remains unknown whether PDA influences the occurrence of postoperative PJI. There is insufficient evidence to support performing this intervention routinely. The health care systems and individual organisations will likely need to make decisions on continuation of such programmes on the basis of this limited amount of information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.003 | 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".