Bibliographic record
Abstract
The number of citations a work has received is a measure of its impact. We identified the top cited works in Parkinson's disease. A Web of Science search was performed for articles including the keyword "Parkinson*" in the title (the asterisk was included in the search string as a wild card character). Articles with more than 400 citations, the threshold to be considered a "citation classic," were identified and analyzed. The 107 articles identified appeared in 33 different journals, with clinical articles primarily appearing in the New England Journal of Medicine and Lancet, and scientific articles primarily in Nature, Science, and the Proceedings of the National Academy of Sciences. There were 52 laboratory studies, 38 clinical studies, 12 review articles, and 5 classifications of disease. The clinical studies included evaluation of medical and surgical therapies, and the laboratory studies included gene discovery, molecular biology, and cellular biology, as well as animal models and neuropathological studies. High impact topics included deep brain stimulation, levodopa therapy and related adverse effects, MPTP-based animal studies, discovery and evaluation of genetic mutations, and pathogenesis related to oxidative degeneration. More than half of the articles identified in this study have been published in the past 20 years. Prior to 1990, highly cited articles in Parkinson's disease tended to be those that evaluated medical therapies and defined the clinical and neuropathological characteristics of the disease. Since 1990, a high proportion of the citation classics address the genetic characterization of and surgical treatments for the disease suggesting that these are the most significant recent developments and main drivers of impact in this field.
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 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.072 | 0.088 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".