An Investigation of Topics and Trends of Tracheal Replacement Studies Using Co-Occurrence Analysis
Bibliographic record
Abstract
This study evaluated tracheal regeneration studies using scientometric and co-occurrence analysis to identify the most important topics and assess their trends over time. To provide the adequate search options, PubMed, Scopus, and Web of Science (WOS) were used to cover various categories such as keywords, countries, organizations, and authors. Search results were obtained by employing Bibexcel. Co-occurrence analysis was applied to evaluate the publications. Finally, scientific maps, author's network, and country contributions were depicted using VOSviewer and NetDraw. Furthermore, the first 25 countries and 130 of the most productive authors were determined. Regarding the trend analysis, 10 co-occurrence terms out of highly frequent words were examined at 5-year intervals. Our findings indicated that the field of trachea regeneration has tested different approaches over the time. In total, 65 countries have contributed to scientific progress both in experimental and clinical fields. Special keywords such as tissue engineering and different types of stem cells have been increasingly used since 1995. Studies have addressed topics such as angiogenesis, decellularization methods, extracellular matrix, and mechanical properties since 2011. These findings will offer evidence-based information about the current status and trends of tracheal replacement research topics over time, as well as countries' contributions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.075 | 0.084 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".