Current review—The rise of bacteriophage as a unique therapeutic platform in treating peri‐prosthetic joint infections
Bibliographic record
Abstract
Peri-prosthetic joint infection (PJI) is one of the most serious and dreaded complications after total joint replacement (TJR). Due to an aging population and the constant rise in demand for TJR, the incidence of PJI is also increasing. Successful treatment of PJI is challenging and is associated with high failure rates. One of the main causes for treatment failure is bacterial biofilm formation on implant surfaces and the adherence of biofilm bacteria on tissue and bone next to the implant. Biofilms are protective shields to bacterial cells and possess many unique properties that leads to antibiotic resistance. New therapeutic platforms are currently being explored to breakdown biofilm matrix in order to enhance the efficacy of antibiotics. Bacteriophages (phages) is one of these unique therapeutic platforms that can degrade biofilms as well as target the killing of bacterial cells. Preclinical studies of biofilm-mediated infections have demonstrated the ability of phage to eradicate biofilms and clear infections by working synergistically with antibiotics. There is strong preclinical evidence that phage can reduce the concentration of antibiotics required to treat an infection. These findings support a promising role for phages as a future clinical adjunct to antibiotics. In addition, phage therapy can be personalized to target a specific bacterial strain. Clinical studies using phage therapy are limited in Western literature; but phase I studies have established good safety profile with no adverse outcomes reported. In order to translate phage therapy to treat PJI in clinics, further preclinical testing is still required to study optimal delivery methods as well as the interaction between phage and the immune system in vivo. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1051-1060, 2018.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".