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Record W3105700917 · doi:10.1016/j.heliyon.2020.e05493

Collaborative scientific production of epilepsy in Latin America from 1989 to 2018: A bibliometric analysis

2020· article· en· W3105700917 on OpenAlexaboutno aff
Cristian Morán‐Mariños, Josmel Pacheco‐Mendoza, Tatiana Metcalf, Walter De la Cruz Ramirez, Carlos Alva‐Díaz

Bibliographic record

VenueHeliyon · 2020
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansBibliometricsScopusLibrary scienceSocioeconomic statusCitationRegional scienceScience Citation IndexGeographyDemographySocioeconomicsPolitical scienceSociologyPopulationMEDLINEComputer science

Abstract

fetched live from OpenAlex

-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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0240.253
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.327
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2020
Admission routes1
Has abstractyes

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