From study to scalpel: knowledge translation for research in orthopaedic surgery
Bibliographic record
Abstract
Evidence-based medicine (EBM) requires current best evidence to support treatment decisions for individuals.1 Despite substantial growth in EBM, medical practice continues to lag behind best evidence, and orthopaedics is no exception. In one study, American patients were only receiving about 57% of recommended care for orthopaedic conditions, indicating a research-to-practice gap.2 Orthopaedic research investigating surgical interventions is time consuming, costly and complex. Additionally, findings must reach a wide population of surgeons and patients to have impact. Researchers commonly rely on two main channels of dissemination: journal publication and conference presentations,3 often only reaching like-minded researchers and clinicians. Knowledge translation (KT) supports EBM and is the synthesis, dissemination, exchange and ethically sound application of knowledge to improve health care.4 Strong KT plans can help secure research funding, scale up interventions, strengthen the quality of research and widen the impact of important studies. A concerted effort to improve KT within orthopaedics has the potential for rapid improvement in the field, by narrowing the knowledge to action gap, influencing decision-makers, and ultimately improving patient outcomes. This editorial will walk readers through five key steps to develop feasible and effective KT plans to disseminate evidence-based best practices in orthopaedic surgery (figure 1). The steps are structured according to John Lavis’ five questions and organisation framework for effective translation of research knowledge5 (box 1). Figure 1 Five steps to creating an effective knowledge translation plan for research in orthopaedic surgery. Box 1 ### Five guiding questions for knowledge translation plans5
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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 | 0.152 | 0.404 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.045 | 0.033 |
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".