Factors influencing the adoption of innovation in spine surgery: An international survey of AO spine network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Knowledge translation from research to clinical practice can often be challenging, and practice modification patterns among surgeons may stem from a variety of sources, including personal experience, peer influence, ongoing education, and evolving research findings. This study aimed to investigate the adoption patterns amongst surgeons for newer innovations and to analyse the factors affecting the implementation of the same in clinical practice. We used the adoption of osteobiologics as a case example. An international expert survey was conducted among AO Spine users and members. The survey, comprising 30 items, explored surgeons' demographics, risk aversion, and factors influencing practice change. We categorized the innovation-adoptive nature of the surgeons and scored their risk-adoptive behaviour. A total of 458 responses were received from surgeons across 81 countries including 433 male (95%), orthopaedic surgeons (n = 263; 57%) from university-affiliated hospitals (n = 185; 40%). Most were in the early majority phase of the innovation-adoption cycle (n = 174; 38%) with a majority in the ‘high-moderate’ risk-adoption category (n = 396; 86%). This risk adoption behaviour had a significant correlation with their appetite for innovation (r = 0.182,p=<0.001). About 67.9% of respondents preferred scientific literature and conference presentations showcasing solid clinical evidence to be the most influential factor in driving change in their clinical practice. Material logistics (55%) is considered an important barrier to practice modification followed by familiarity (50%) and financial reimbursements (25%). A complex interplay exists between risk-adoptive behaviour amongst surgeons and the factors influencing a change in their clinical practice. Although most surgeons were in the early adoptive phase in accepting the innovations into their clinical practice, they were also equally noted to be risk tolerant. Hence, a successful adoption of practice-changing innovation hinges on addressing not only logistical and financial challenges but also on providing robust scientific evidence to drive the necessary change in clinical practice. • Most spine surgeons are in the early majority phase of innovation-adoption with moderate risk-adoption behaviour. • Risk adoption behaviour significantly correlates with their appetite for innovation. • Most surgeons find scientific literature and conference presentations with solid clinical evidence most influential. • Material logistics is a key barrier to practice modification, followed by familiarity and financial reimbursements. • A complex interplay exists between risk-adoptive behavior among surgeons and factors influencing clinical practice change.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it