Global Practices and Preferences in the Use of Osteobiologics for Anterior Cervical Discectomy and Fusion: A Cross-Sectional Study
Bibliographic record
Abstract
Study Design Cross-sectional study. Objectives To assess global practices and preferences in the use of osteobiologics for anterior cervical discectomy and fusion (ACDF) and identify factors influencing the choice of specific osteobiologics. Methods An online survey developed by AO Spine was distributed to spine surgeons worldwide. The survey captured demographic characteristics, osteobiologic use and related information (i.e., previous training, practice patterns, etc.), and factors influencing osteobiologic choice in ACDF. Descriptive statistics, Chi-square tests, and multiple logistic regression were used to analyze responses, focusing on the associations between osteobiologic use and variables such as training, cost awareness, and regional practices. Results Responses from 458 surgeons revealed regional variability in osteobiologic preferences. Autologous iliac crest bone graft (AICBG) was predominant in Asia Pacific and Middle East, while allograft and demineralized bone matrix were favored in North America and Latin America ( P < 0.0001). Over half of the respondents (79.7%) lacked formal training in osteobiologics, and 53.1% were unaware of related costs. Surgeons residing in the Asia Pacific region (OR: 0.47, 95% CI: 0.26-0.84, P = 0.0114), without formal training (OR: 0.53, 95% CI: 0.29-0.97, P = 0.0429), or using cages less often (OR: 0.15, 95% CI: 0.06-0.34, P < 0.0001) were less likely to utilize osteobiologics. Osteobiologic use was also more common when related costs were not an issue for the practitioner (OR: 2.32, 95% CI:1.47-3.70, P = 0.0004). Conclusions Significant variation exists in osteobiologic use in ACDF across global regions, influenced by surgeon training, cost awareness, and institutional resources. Enhanced training and guidelines could improve consistency in osteobiologic application.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".