AO Spine Knowledge Forums Promote Collaboration and Elevate the Impact of Research: A Bibliometric Analysis
Bibliographic record
Abstract
Study Design Bibliometric analysis. Objectives This study used bibliometric analyses to characterize the effect of AO Spine Knowledge Forum (KF) participation on publication trends among members. We examined associations of membership in KF organizations with academic productivity, collaboration, and scientific impact. Methods We queried the Web of Science database for publications by members of KF Tumor (N = 58), KF Trauma and Infection (N = 45), KF Spinal Cord Injury (N = 38), KF Degenerative (N = 54), and KF Deformity (N = 55). Resulting metadata were exported; statistical and bibliometric analyses were performed using Python packages. Results Our query returned 24,267 articles by KF members, of which 18,804 were identified as relevant to respective organizational themes through an algorithmic analysis of titles and abstracts. These works, published between 1980 and 2025, included contributions from 67,895 authors. Research productivity, co-authorship among members ( P < 0.001), unique institutional affiliations per article ( P < 0.001), and international collaboration increased contemporaneously with the first KF formation (2010). A positive association was found between the number of KF authors per publication and source journal impact factor ( P < 0.001). Term analysis highlighted research foci within each KF and influential publications were identified. Conclusions These findings suggest that formalization of researcher relationships and the research infrastructure and support provided by the KF model was associated with increased and more impactful research output and collaboration. The KF model could be applied in other organizations whose mission includes collaborative research. Methods used in this study are easily replicable and may be applied to investigate the impact of other professional organizations across various fields.
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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 | 0.051 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.089 | 0.105 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".