Dissemination and Implementation Science in Plastic and Reconstructive Surgery: Perfecting, Protecting, and Promoting the Innovation That Defines Our Specialty
Bibliographic record
Abstract
SUMMARY: Plastic and reconstructive surgery has an illustrious history of innovation. The advancement, if not the survival, of the specialty depends on the continual development and improvement of procedures, practices, and technologies. It follows that the safe adoption of innovation into clinical practice is also paramount. Traditionally, adoption has relied on the diffusion of new knowledge, which is a consistent but slow and passive process. The emerging field of dissemination and implementation science promises to expedite the spread and adoption of evidence-based interventions into clinical practice. The field is increasingly recognized as an important function of academia and is a growing priority for major health-related funding institutions. The authors discuss the contemporary challenges of the safe implementation and dissemination of new innovations in plastic and reconstructive surgery, and call on their colleagues to engage in this growing field of dissemination and implementation science.
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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 | 0.004 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".