Bibliometric analysis of the usage of tenecteplase for stroke
Bibliographic record
Abstract
INTRODUCTION: In recent years, tenecteplase has been competing with alteplase as a treatment for acute ischemic stroke given its ease of administration, lower dosage, cost-effectiveness, and better safety data. This paper seeks to analyze academic literature regarding the burgeoning usage of tenecteplase as a treatment for acute ischemic stroke across the world. METHOD: The Web of Science database was used to collect the data from articles containing the keywords "Tenecteplase" and "Stroke" published from 1999 to 2023. The search resulted in 576 journal articles. This study analyzed metadata related to the country, institution, keywords, and date published for each article in the database pertaining to tenecteplase use for stroke. RESULTS: The United States led in publications (260, 39.93%), followed by Australia (101, 15.51%), and a tie for third place between Canada and China (77, 11.83% each). The three most prevalent keywords were tenecteplase (N = 324), alteplase (N = 284), and thrombolysis (N = 244). The University of Melbourne and the University of Calgary were the leading institutions publishing on the use of tenecteplase as a treatment for stroke. In 2023, the number of publications on the usage of tenecteplase for stroke was the greatest, making up 24.3% of all papers on the topic. CONCLUSION: The surge in academic papers regarding tenecteplase in stroke in 2023 could be a good indicator of the drug's increasing prevalence as a treatment for stroke. Despite this finding, tenecteplase is currently not an FDA-approved therapy in the US as Genentech, the drug's manufacturer, has yet to file for federal approval for acute ischemic stroke treatment.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.149 | 0.192 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".