Trends of spinal tuberculosis research (1994–2015)
Bibliographic record
Abstract
BACKGROUND: Spinal tuberculosis is the most common form of skeletal tuberculosis. However, there were limited data to evaluate the trend of spinal tuberculosis research. This study aims to investigate the trend of spinal tuberculosis research and compare the contribution of research from different countries and authors. METHODS: Spinal tuberculosis-related publications from 1994 to 2015 were retrieved from the Web of Science database. Excel 2013, GraphPad Prism 5, and VOSviewer software were used to analyze the search results for number of publications, cited frequency, H-index, and country contributions. RESULTS: A total of 1558 papers were identified and were cited 16,152 times as of January 25, 2016. The United States accounted for 15.1% of the articles, 22.3% of the citations, and the highest H-index (33). China ranked third in total number of articles, fifth in citation frequency (815), and ranked seventh in H-index (13). The journal Spine (IF 2.297) had the highest number of publications. The author Jain A.K. has published the most papers in this field (20). The article titled "Tuberculosis of the spine: Controversies and a new challenge" was the most popular article and cited a total of 1138 times. The keyword "disease" was mentioned the most for 118 times and the word "bone fusion" was the latest hotspot by 2015. CONCLUSION: Literature growth in spinal tuberculosis is slowly expanding. Although publications from China are increasing, the quality of the articles still requires improvements. Meanwhile, the United States continues to be the largest contributor in the field of spinal tuberculosis. According to our bibliometric study, bone fusion may be an emerging topic within spinal tuberculosis research and is something that should be closely observed.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.033 | 0.044 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".