Recommendations for design and conduct of preclinical in vivo studies of orthopedic device‐related infection
Bibliographic record
Abstract
Orthopedic device-related infection (ODRI), including both fracture-related infection (FRI) and periprosthetic joint infection (PJI), remain among the most challenging complications in orthopedic and musculoskeletal trauma surgery. ODRI has been convincingly shown to delay healing, worsen functional outcome and incur significant socio-economic costs. To address this clinical problem, ever more sophisticated technologies targeting the prevention and/or treatment of ODRI are being developed and tested in vitro and in vivo. Among the most commonly described innovations are antimicrobial-coated orthopedic devices, antimicrobial-loaded bone cements and void fillers, and dual osteo-inductive/antimicrobial biomaterials. Unfortunately, translation of these technologies to the clinic has been limited, at least partially due to the challenging and still evolving regulatory environment for antimicrobial drug-device combination products, and a lack of clarity in the burden of proof required in preclinical studies. Preclinical in vivo testing (i.e. animal studies) represents a critical phase of the multidisciplinary effort to design, produce and reliably test both safety and efficacy of any new antimicrobial device. Nonetheless, current in vivo testing protocols, procedures, models, and assessments are highly disparate, irregularly conducted and reported, and without standardization and validation. The purpose of the present opinion piece is to discuss best practices in preclinical in vivo testing of antimicrobial interventions targeting ODRI. By sharing these experience-driven views, we aim to aid others in conducting such studies both for fundamental biomedical research, but also for regulatory and clinical evaluation. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:271-287, 2019.
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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 | 0.075 | 0.091 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.035 |
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".