AO Spine Guideline for the Use of Osteobiologics (AOGO) in Anterior Cervical Discectomy and Fusion for Spinal Degenerative Cases
Bibliographic record
Abstract
STUDY DESIGN: Guideline. OBJECTIVES: To develop an international guideline (AOGO) about the use of osteobiologics in anterior cervical discectomy and fusion (ACDF) for treating degenerative spine conditions. METHODS: The guideline development process was guided by AO Spine Knowledge Forum Degenerative (KF Degen) and followed the Guideline International Network McMaster Guideline Development Checklist. The process involved 73 participants with expertise in degenerative spine diseases and surgery from 22 countries. Fifteen systematic reviews were conducted addressing respective key topics and evidence was collected. The methodologist compiled the evidence into GRADE Evidence-to-Decision frameworks. Guideline panel members judged the outcomes and other criteria and made the final recommendations through consensus. RESULTS: Five conditional recommendations were created. A conditional recommendation is about the use of allograft, autograft or a cage with an osteobiologic in primary ACDF surgery. Other conditional recommendations are about the use of osteobiologic for single- or multi-level ACDF, and for hybrid construct surgery. It is suggested that surgeons use other osteobiologics rather than human bone morphogenetic protein-2 (BMP-2) in common clinical situations. Surgeons are recommended to choose 1 graft over another or 1 osteobiologic over another primarily based on clinical situation, and the costs and availability of the materials. CONCLUSION: This AOGO guideline is the first to provide recommendations for the use of osteobiologics in ACDF. Despite the comprehensive searches for evidence, there were few studies completed with small sample sizes and primarily as case series with inherent risks of bias. Therefore, high-quality clinical evidence is demanded to improve the guideline.
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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 | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".