Research trends in trabecular bone score
Bibliographic record
Abstract
Objective: Trabecular bone score (TBS) is a novel index for assessing bone microarchitecture quality. No bibliometric reviews to date have yet explored the literature of TBS. Therefore, this study aimed to provide a bibliometric review on the trends of research publications on TBS indexed in the Science Citation Index Expanded and the Social Sciences Citation Index from 2008 to 2019. Materials and Methods: Using the Science Citation Index Expanded and the Social Sciences Citation Index, articles designated as original articles or review articles were searched using the keyword “trabecular bone score.” The retrieved articles were analyzed using Histcite 12.03.17 and VOSviewer v. 1.6.15 to identify top authors, journals, countries, and occurrence of keywords. Results: A total of 430 original and review articles on TBS published between 2008 and 2019 were identified. The number of articles increased steadily from 2008 to 2019, reaching 80 articles in 2019 alone. The United States of America, Switzerland, and France were the countries with the highest output of publications. The journal Osteoporosis International published the largest number of articles on TBS. Analysis of co-occurrence of author-supplied keywords revealed four clusters, with TBS, bone mineral density, and osteoporosis as the most prominent keywords. Conclusion: This bibliometric study on TBS published between 2008 and 2019 revealed the collaborative network of countries and the highly published journals and authors. Co-occurrence of keywords also revealed clusters of research hotspots, which could contribute to the understanding of the current state of TBS research and the identification of research gap.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.063 | 0.086 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".