Global academic output on COVID-19 and Guillain-Barre Syndrome: A bibliometric analysis
Bibliographic record
Abstract
The purpose of this study was to bibliometrically analyze scientific publications on Guillain-Barré syndrome (GBS) related to COVID-19. A specialized search of the Scopus was used (December 2019 to February 2022). Collected publications were evaluated in Scival (Elsevier). The results were arranged in tables for presentation. We found 959 papers that were collected and the highest percentage of these belonged to the area of Neurology. Josef Finsterer was the author with the highest academic production, but Benedict Michael was the one with the highest impact worldwide. Although the Universidade Federal de São Paulo (Brazil) was the college with the highest scientific production, it was King's College London that reported the highest impact. Regarding the journals, the Journal of Neurology is the one with the highest worldwide production. In addition, an increase in first quartile publication and articles with national collaboration was reported. Scholarly output on COVID-19 and GBS have been increasing. Although national collaboration has the highest proportion of manuscripts, it is the international type that reported a greater impact, this would show a great interest on the part of researchers from all over the world regarding this topic.
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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 | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.042 | 0.065 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".