An international analysis of stem cell research in intervertebral disc degeneration
Bibliographic record
Abstract
Stem cell therapy has been increasingly investigated as a promising strategy for intervertebral disc degeneration (IDD). However, no international analysis of stem cell research has yet been conducted. This study aimed to analyze the major characteristics of published reports of stem cell use for IDD and to present a global insight into stem cell research. The study period spanned from the inception of the Web of Science database to 2021. A search strategy using specific keywords was implemented to retrieve relevant publications. The numbers of documents, citations, countries, journals, article types, and stem cell types were evaluated. A total of 1170 papers were retrieved. The analysis showed a significant increase in the number of papers over time (p < 0.001). High-income economies accounted for the majority of papers (758, 64.79 %). China produced the most articles (378, 32.31 %), followed by the United States (259, 22.14 %), Switzerland (69, 5.90 %), United Kingdom (54, 4.62 %), and Japan (47, 4.02 %). The United States ranked first in terms of the number of citations (10,346), followed by China (9177) and Japan (3522). Japan ranked first in terms of the number of citations per paper (74.94), followed by United Kingdom (58.54) and Canada (53.74). When standardized by population, Switzerland ranked first, followed by Ireland and Sweden. When gross domestic product was considered, Switzerland ranked first, followed by Portugal and Ireland. The number of papers was positively correlated with gross domestic product (p < 0.001, r = 0.673); however, there was no significant correlation with population (p = 0.062, r = 0.294). Mesenchymal stem cells were the most investigated stem cells, followed by nucleus pulposus-derived stem cells and adipose-derived stem cells. A sharp increase in stem cell research was observed in the field of IDD. China produced the most, although several European countries were more productive relative to their populations and economies.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.075 | 0.114 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".