2021 Frank Stinchfield Award: A novel cemented hip hemiarthroplasty infection model with real-time in vivo imaging in rats
Bibliographic record
Abstract
Aims The aims of this study were to develop an in vivo model of periprosthetic joint infection (PJI) in cemented hip hemiarthroplasty, and to monitor infection and biofilm formation in real-time. Methods Sprague-Dawley rats underwent cemented hip hemiarthroplasty via the posterior approach with pre- and postoperative gait assessments. Infection with Staphylococcus aureus Xen36 was monitored with in vivo photoluminescent imaging in real-time. Pre- and postoperative gait analyses were performed and compared. Postmortem micro (m) CT was used to assess implant integration; field emission scanning electron microscopy (FE-SEM) was used to assess biofilm formation on prosthetic surfaces. Results All animals tolerated surgery well, with preservation of gait mechanics and weightbearing in control individuals. Postoperative in vivo imaging demonstrated predictable evolution of infection with logarithmic signal decay coinciding with abscess formation. Postmortem mCT qualitative volumetric analysis showed high contact area and both cement-bone and cement-implant interdigitation. FE-SEM revealed biofilm formation on the prosthetic head. Conclusion This study demonstrates the utility of a new, high-fidelity model of in vivo PJI using cemented hip hemiarthroplasty in rats. Inoculation with bioluminescent bacteria allows for non-invasive, real-time monitoring of infection. Cite this article: Bone Joint J 2021;103-B(7 Supple B):9–16.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".