A Comprehensive and Historical Review of Minimally Invasive Scoliosis Surgery in Adolescent Idiopathic Scoliosis: An Analysis of Research Trends and Hotspots
Bibliographic record
Abstract
Over the past two decades, interest in minimally invasive scoliosis surgery (MISS) for adolescent idiopathic scoliosis (AIS) has grown substantially, driven by advancements in growth-based surgical techniques. Given the substantial advancements in MISS for AIS, investigating the bibliometric data of the scientific literature is crucial to understanding the current research trend and providing valuable insights into its future directions. However, limited information on MISS for AIS exists in the literature. The publication data related to MISS for AIS from 2004 to 2024 were exported from the Web of Science. The research output between 2004 and 2024 was 373 for publication volume, 7760 for citations, and 46 for h-index. The annual publication and citation trend over time showed a gradual increase with fluctuations up until 2017, followed by a sharp upward trend starting in 2018. The foremost countries and affiliations in this field were the United States and Montreal University in Canada, respectively. The top 10 most-cited articles on MISS in AIS predominantly focused on the topic of vertebral body tethering (VBT). Among the productive authors, most contributions were focused on VBT, while several authors in South Korea significantly contributed to the study of MISS via a posterior approach. Historical development of VBT and posterior MISS identified their current advantages and limitations and highlighted potential future research directions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".