An Overview of Tracheal Stenosis Research Trends and Hot Topics.
Bibliographic record
Abstract
BACKGROUND: Tracheal stenosis remains a challenge in the thoracic surgery field. Recognizing the hot topics and major concepts in this area would help the health policy makers to determine their own priorities and design the effective research plans. The present study analyzed and mapped the topics and trends of tracheal stenosis studies over time as well as authors' and countries' contributions. MATERIALS AND METHODS: Search results were obtained employing Bibexcel. To determine cold and hot topics, co-occurrence analysis was applied using three international databases 'Web of Science', 'PubMed' and 'Scopus'. Appropriately, different categories in the articles such as keywords, authors, and countries were explored via VOSviewer and NetDraw. Afterward, the trends of research topics were depicted in four time-intervals from 1945 to 2015 by ten co-occurrence terms. RESULTS: The majority of articles were limited to case series and retrospective studies. The studies had been conducted less frequently on prevention, risk factors and incidence determination but extensively on treatment and procedures. Based on the articles indexed in WOS, 45 countries and 8,260 authors have contributed to scientific progress in this field. The highest degree of cooperation occurred between the USA and England with 15 common papers. CONCLUSIONS: Most of the published literature in tracheal stenosis research field was about surgical and non-surgical treatments. Conducting the screening and prevention studies would diminish the burden of this disease on the health system as well as the patients and their families' well-being.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".