Development of an International AO Spine Guideline for the Use of Osteobiologics in Anterior Cervical Fusion and Decompression (AO-GO)
Bibliographic record
Abstract
STUDY DESIGN: Methodological study for guideline development. OBJECTIVE: AO Spine Guideline for Using Osteobiologics (AO-GO) project for spine degenerative pathologies was an international, multidisciplinary collaborative initiative to identify and evaluate evidence on existing use of osteobiologics in Anterior Cervical Fusion and Decompression (ACDF). The aim was to formulate precisely defined, clinically relevant and internationally applicable guidelines ensuring evidence-based, safe and effective use of osteobiologics, considering regional preferences and cost-effectiveness. METHODS: Guideline was completed in two phases: Phase 1- evidence synthesis; Phase 2- recommendation development based on the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. In Phase 1, key questions identified by a panel of experts were addressed in a series of systematic reviews of randomized and non-randomized studies. In Phase 2, the GRADE approach was used to formulate a series of recommendations, including expert panel discussions via web calls and face-to-face meetings. DISCUSSION: AO-GO aims to bridge an important gap between evidence and use of osteobiologics in spine fusion surgeries. Owing to differences in osteobiologics preparation and functional characteristics, regulatory requirements for approval may vary, therefore it is highly likely that these products enter market without quality clinical trials. With a holistic approach the guideline aims to facilitate evidence-based, patient-oriented decision-making processes in clinical practice, thus stimulating further evidence-based studies regarding osteobiologics usage in spine surgeries. In Phase 3, the guideline will be disseminated and validated using prospectively collected clinical data in a separate effort of the AO Spine Knowledge Forum Degenerative in a global multicenter clinical study.
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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 | 0.121 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".