Parkinson's disease: Evolution of the scientific literature from 1983 to 2017 by countries and journals
Bibliographic record
Abstract
This study charts the evolution of the scientific literature on Parkinson's disease (PD) from 1983 to 2017 to inform communities of scientists, physicians, patients, caregivers and politicians concerned with PD. Articles published in journals indexed in the Science Citation Index-Expanded database of the Web of Science were retrieved and analyzed in seven five-year periods: 1983-1987, 1988-1992, 1993-1997, 1998-2002, 2003-2007, 2008-2012 and 2013-2017. Over 35 years the number of research papers on PD increased 33-fold: 885 papers in 1983-1987 to 29,972 in 2013-2017. At the same time the number of countries contributing to PD research increased from 37 to 131. The USA was the most prolific country throughout, followed by several European (UK, Germany, Italy and France) and English-speaking (Canada and Australia) countries. By 2003, several Asian countries (China, South Korea, India and Turkey) emerged with rapid increases in publications related to PD. By 2013-2017, China surpassed all but the USA to rank 2nd globally in productivity. Despite an increase from 4 to 22 African countries publishing PD research from 1983 to 2017, most were either unproductive or contributed ≤5 papers in each five-year period. There has also been a 12-fold increase in the number of journals (232-2824) containing papers on PD. In 2013-2017 three PD-focused journals (Parkinsonism & Related Disorders, Movement Disorders and Journal of Parkinson's Disease) contained 6.8% of all PD papers while a large majority (82.5%) of journals published ≤ 10 papers. This quantitative study complements the numerous extant qualitative reviews to provide a global perspective on PD research.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.074 | 0.102 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".