Global research characteristics and trends of infection after spinal implant surgery: a bibliometric analysis
Bibliographic record
Abstract
Background: With the growing awareness of postoperative infection, increasing focus has been placed on infection after spinal implant surgery (IASIS). This study aimed to explore the development and trends of research regarding IASIS using bibliometric analysis. Methods: Scientific articles on IASIS research published between February 1, 2000, and December 31, 2020 were retrieved from the Web of Science database. Results: A total of 820 publications were included in the bibliometric analysis, with studies originating from 46 countries and 6 languages. Researchers from the United States published the highest number of articles and collaborated closely with researchers in Canada, Germany, and Japan. The author with the most publications was Alexander R. Vaccaro. The journal with the most articles and citations was Spine. Most of the research was performed on risk factors and the incidence of IASIS. Co-occurrence analysis revealed that the most recent research trend was likely related to the management of IASIS and the international consensus meeting. Three clusters of research were identified through a thematic map: diagnosis and treatment of IASIS, scoliosis-related infection, and risk factors and prevention of IASIS. Conclusions: Research on IASIS increasingly grew between 2000 and 2020. Spinal surgeons and institutes from the United States had the highest number of publications and academic impact in this field. Diagnosis-related problems and multidisciplinary work on IASIS require further attention in the future. Current trends in IASIS are likely associated with IASIS management and the international consensus meeting.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.113 | 0.147 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".