Research Practices and Needs Among Spine Surgeons Worldwide
Bibliographic record
Abstract
Objective Resource allocation to research activities is challenging and there is limited evidence to justify decisions. Members of AO Spine were surveyed to understand the research practices and needs of spine surgeons worldwide. Methods An 84-item survey was distributed to the AO Spine community in September of 2020. Respondent demographics and insights regarding research registries, training and education, mentorship, grants and financial support, and future directions were collected. Responses were anonymous and compared among regions. Results A total of 333 spine surgeons representing all geographic regions responded; 52.3% were affiliated with an academic/university hospital, 91.0% conducted clinical research, and 60.9% had 5+ years of research experience. There was heterogeneity among research practices and needs across regions. North American respondents had more research experience ( P = .023), began conducting research early on ( P < .001), had an undergraduate science degree ( P < .001), and were more likely to have access to a research coordinator or support staff ( P = .042) compared to other regions. While all regions expressed having the same challenges in conducting research, Latin America, and Middle East/Northern Africa respondents were less encouraged to do research ( P < .001). Despite regional differences, there was global support for research registries and research training and education. Conclusion To advance spine care worldwide, spine societies should establish guidelines, conduct studies on pain management, and support predictive analytic modeling. Tailoring local/regional programs according to regional needs is advised. These results can assist spine societies in developing long-term research strategies and provide justified rationale to governments and funding agencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".