An Assessment of the World’s Contribution to Spine Trauma Care: A Bibliometric Analysis of Classifications and Surgical Management; An AO Spine Knowledge Forum Trauma Initiative
Bibliographic record
Abstract
STUDY DESIGN: Bibliometric analysis. OBJECTIVES: An analysis of the literature related to the assessment and management of spinal trauma was undertaken to allow the identification of top contributors, collaborations and research trends. METHODS: A search to identify original articles published in English between 2011 and 2020 was done using specific keywords in the Web of Science database. After screening, the top 300 most cited articles were analyzed using Biblioshiny R software. RESULTS: The highest number of contributions were from the Thomas Jefferson University, USA, University of Toronto and University of British Columbia, Canada. The top 3 most prolific authors were Vaccaro AR, Arabi B, and Oner FC. The USA and Canada were among the top contributing countries; Switzerland and Brazil had most multiple country co-authored articles. The most relevant journals were the European Spine Journal, Spine and Spine Journal. Three of the 5 most cited articles were about classification systems of fractures. The keyword analysis included clusters for different spinal regions, spinal cord injury, classification agreement and reliability studies, imaging related studies, surgical techniques and outcomes. CONCLUSIONS: The study identified the most impactful authors and affiliations, and determined the journals where most impactful research is published in the field. Study also compared the productivity and collaborations across countries. The study highlighted the impact of development of new classification systems, and identified research trends including instrumentation, fixation and decompression techniques, epidemiology and recovery after spinal trauma.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.040 | 0.188 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".