The Microbial Revolution in the World of Joint Replacement Surgery
Bibliographic record
Abstract
Background: The prevalence of revision surgery due to aseptic loosening and periprosthetic joint infection (PJI) following total hip and knee arthroplasty is growing. Strategies to prevent the need for revision surgery and its associated health-care costs and patient morbidity are needed. Therapies that modulate the gut microbiota to influence bone health and systemic inflammation are a novel area of research. Methods: A literature review of preclinical and clinical peer-reviewed articles relating to the role of the gut microbiota in bone health and PJI was performed. Results: There is evidence that the gut microbiota plays a role in maintaining bone mineral density, which can contribute to osseointegration, osteolysis, aseptic loosening, and periprosthetic fractures. Similarly, the gut microbiota influences gut permeability and the potential for bacterial translocation to the bloodstream, increasing susceptibility to PJI. Conclusions: Emerging evidence supports the role of the gut microbiota in the development of complications such as aseptic loosening and PJI after total hip or knee arthroplasty. There is a potential for microbial therapies such as probiotics or fecal microbial transplantation to moderate the risk of developing these complications. However, further investigation is required. Clinical Relevance: Modulation of the gut microbiota may influence patient outcomes following total joint arthroplasty.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".