Collaborative scientific production of epilepsy in Latin America from 1989 to 2018: A bibliometric analysis
Bibliographic record
Abstract
Socioeconomic and cultural factors coupled with an inability to control many endemic and emerging diseases have resulted in a growing incidence of epilepsy cases in Latin America. This study aimed to analyze and describe scientific research output trends in epilepsy research for the period 1989–2018. Publications were extracted from Scopus indexed journals. Bibliometric analysis was used to analyze scientific output including number of annual publications, documents, and publication characteristics. A mapping analysis using VOSviewer software visualized collaborative network analysis, co-citation analysis, and keyword co-occurrence analysis. SciVal quantitatively analyzed distribution of countries, institutions, citation counts, H -index, and research collaborative partnerships. A total of 176507 records were initially retrieved after which 5636 were analyzed. Overall, an increasing trend for publication output was observed from 19 articles in 1989 to 342 in 2018; the number of publications significantly increased over the past 20 years (p = 0.0065). The majority of publications were original articles (74.4%). Brazil had the most scientific production (55.2%), followed by Mexico (15.4%) and Argentina (10%). Extra-regional collaboration was primarily with the United States, United Kingdom, and Canada; intraregional collaboration was low. The most common area of investigation by co-occurrences was "diagnostic research" (37.2%), with studies based on electroencephalography and nuclear magnetic resonance. Epilepsy research in Latin America has seen a steady growth with significant increases over the past 20 years. Brazil, Mexico, and Argentina are the most productive countries in the field collaborating primarily with extra-regional countries of high-income.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.106 | 0.182 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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, 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".