A Scientometric Analysis of the 100 Most Cited Articles on Pallidotomy
Bibliographic record
Abstract
BACKGROUND: Pallidotomy is the oldest stereotactically performed neurosurgical procedure for movement disorders. Consequently, there is a wealth of literature available on the topic. OBJECTIVES: The aim of this analysis was to identify the top-cited articles on pallidotomy in order to discern the origins, spread, the current trends, and the future directions of this surgical procedure. METHODS: We performed a search of the Web of Science database on 19 October 2020 using the keyword "pallidotomy." The top-100 cited articles found were arranged in descending order on the basis of citation count (CC) and citation per year (CY). Relevant conclusions were derived. RESULTS: The 100 top-cited articles were published between 1961 and 2017, in 24 journals. The average CC and CY were 118.1 (range - 856-46) and 5.326 (range - 29.52-2.09), respectively. The 3 most prolific authors were Lang AE (Neurologist - Toronto), Lozano AM (Neurosurgeon - Toronto), and Vitek JL (Neurologist - Atlanta). The Journal of Neurosurgery published the highest number of top-cited articles [Neurology. 1960;10:61-9]. The maximum articles were from the USA. University of Toronto and Emory University were the most productive institutions. CONCLUSIONS: Pallidotomy has gone through several ebbs and flows. Unilateral pallidotomy is currently recommended for the treatment of motor symptoms of Parkinson's disease and dystonia. The need for further research and improved technology to make the technique safer and prove its efficacy is highlighted, especially keeping in mind a large number of populations to which the prohibitively expensive deep brain stimulation is unavailable.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.176 | 0.153 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".