Measuring Impact in Stereotactic and Functional Neurosurgery: An Analysis of the Top 100 Most Highly Cited Works and the Citation Classics in the Field
Bibliographic record
Abstract
BACKGROUND: Functional neurosurgery is a rapidly expanding field, with an exponentially growing literature. However, as with other fields, it can sometimes be difficult to distinguish between what is incremental and what is transformational. One way of measuring durable impact is examining the number of times a specific piece of scholarship is cited by others in the field. For example, papers that have been cited at least 400 times are designated 'citation classics' or works that, by virtue of very high citations, have been deemed of particular importance by researchers working in related disciplines. METHODS: We queried a large, web-based scholarly database using 49 pre-selected search terms. The results for each individual query was manually examined for relevance to the functional neurosurgery field in order to arrive at the top 100 most highly cited papers as well as the citation classics. RESULTS: The top 100 most cited papers, including 61 citation classics, in the stereotactic and functional neurosurgery field can be divided into 7 categories: functional/anatomic studies, technological innovations, and papers relevant to movement disorders, pain, psychiatry, radiosurgery and epilepsy. CONCLUSIONS: We have attempted to ascertain which papers have had, and continue to have, significant impact in our rapidly advancing field. At a minimum, the citation classics in functional neurosurgery provide both trainees and seasoned surgeons with a reading list of the 'must-know' works in the field - works whose influence have helped shape the direction of functional neurosurgery well into the future.
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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 | 0.021 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.148 | 0.163 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".