Supplementary Material for: A Scientometric Analysis of the 100 Most Cited Articles on Pallidotomy
Bibliographic record
Abstract
<b><i>Background:</i></b> Pallidotomy is the oldest stereotactically performed neurosurgical procedure for movement disorders. Consequently, there is a wealth of literature available on the topic. <b><i>Objectives:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Conclusions:</i></b> 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
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: Dataset About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.030 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.711 | 0.001 |
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".