Stroke and Myocardial Infarction: A Comparative Systematic Evaluation of Gender-Specific Analysis, Funding and Authorship Patterns in Cardiovascular Research
Bibliographic record
Abstract
BACKGROUND: Major gender differences exist in cardiovascular diseases and lead to different outcomes in women and men. However, attention and incorporation of sex-/gender-specific research might vary among disciplines. We therefore conducted a systematic review comparing publication characteristics and trends between stroke and myocardial infarction (MI) with respect to sex- and gender-related aspects. METHODS: A systematic literature search was performed in PubMed to identify gender-/sex-related articles published for stroke and MI between 1977 and 2008. A specifically designed text mining program was used, and all literature was rated by two independent investigators. Publications were classified according to type of research performed, publication year, funding, geographical location, and gender of first and last authors. RESULTS: 962 articles were retrieved and limited to 405 (42%) gender-relevant publications; 131 on stroke and 274 on MI. Type of performed research differed, especially in disease management, which received little attention (17%) in stroke, while representing the major focus in MI (40%). In both areas, clinical presentation received little attention (3 and 5%). Although publications progressively increased in both fields, an 8- to 10-year time gap emerged for stroke compared to MI. Last authors in both areas were predominantly men, but female last authorship is increasing more significantly over time in the field of stroke. Research on sex and gender differences in MI and stroke is largely underfunded, particularly by the EU. CONCLUSIONS: The data demonstrate how sex-/gender-specific research differs between specialties, most likely due to the diverse interest, funding opportunities and authorship distributions identified.
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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".