Preclinical models of orthopaedic trauma: Orthopaedic Research Society (ORS) and Orthopaedic Trauma Association (OTA) symposium 2022
Bibliographic record
Abstract
Orthopaedic trauma remains a leading cause of patient morbidity, mortality, and global health care burden. Although significant advances have been made in the diagnosis, treatment, and rehabilitation of these injuries, complications such as malunion, nonunion, infection, disuse muscle atrophy and osteopenia, and incomplete return to baseline function still occur. The significant inherent clinical variability in fracture care such as differing patient demographics, injury patterns, and treatment protocols make standardized and replicable study, especially of cellular and molecular based mechanisms, nearly impossible. Hence, the scientists dedicated to improving therapy and treatments for patients with orthopaedic trauma rely on preclinical models. Preclinical models have proven to be invaluable in understanding the timing between implant insertion and bacterial inoculation on the bioburden of infection. Posttraumatic arthritis (PTOA) can take years to develop clinically, but with a porcine pilon fracture model, posttraumatic arthritis can be reliably induced, so different surgical and therapeutic strategies can be tested in prevention. Conversely, the racehorse presents a well-accepted model of naturally occurring PTOA. With preclinical polytrauma models focusing on chest injury, abdominal injury, multiple fractures, and/or head injury, one can study how various injury patterns affect fracture healing can be systemically studied. Finally, these preclinical models serve as a translational bridge to for clinical application in human patients. With selection of the right preclinical model, studies can build a platform to decrease the risk of emerging technologies and provide foundational support for therapeutic clinical trials. In summary, orthopaedic trauma preclinical models allow scientists to simplify a complex clinical challenge, to understand the basic pathways starting with lower vertebrate models. Then, R&D efforts progress to higher vertebrate models to build in more complexity for translation of findings to the clinical practice.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".