Covid-19 and Neurosciences: A Scientometric assessment of Global publications output during 2020–2021
Bibliographic record
Abstract
This study analyses the research output in Covid-19 and Neurosciences during the period 2020–2021 and the analyses included global publications' share, citation impact, the share of countries, and patterns of research communication in most productive journals. It also analyses the characteristics of most productive institutions, authors, and high-cited papers. The most contributed countries are: the USA with 1307 publications followed far by the UK with 571, Italy with 541, China with 372, Germany with 286, India with 285, Canada with 272, Spain with 260, France with 212 and Brazil with 196 articles, etc. the prolific organizations are: Harvard Medical School from the USA (with 124 papers) and received 1200 Citations, followed by INSERM, France (with 87 papers) and received 881 Citations, University of Toronto, Canada (with 86 papers) and received 1720 Citations, King's College London, UK (with 77 papers), University College London with 64, the University of Oxford with 64, Massachusetts General Hospital with 54. The most preferred journals are Psychiatry Research with 270 articles followed by Frontiers In Neurology with 155, Lancet Psychiatry with 149, Brain Behavior And Immunity with 136, Journal Of Neurology with 135, Peerj with 132, and Multiple Sclerosis And Related Disorders with 119. There is an urgent need to substantially increase the research activities in the field of Covid-19 and Neurosciences during the pandemic period.
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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 | 0.000 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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, 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".