Citation classics in neuro-oncology: assessment of historical trends and scientific progress
Bibliographic record
Abstract
BACKGROUND: Citation classics represent the highest cited works in a field and are often regarded as the most influential literature. Analyzing thematic trends in citation classics across eras enables recognition of important historical advances within a field. We present the first analysis of the citation classics in neuro-oncology. METHODS: The Web of Science database was searched using terms relevant to "neuro-oncology." Articles with >400 citations were identified and the top 100 cited articles were evaluated. RESULTS: The top 100 neuro-oncology citation classics consisted of 43 clinical studies (17 retrospective, 10 prospective, 16 randomized trials), 43 laboratory investigations, 8 reviews/meta-analyses, and 6 guidelines/consensus statements. Articles were classified into 4 themes: 13 pertained to tumor classification, 37 to tumor pathogenesis/clinical presentation, 6 to imaging, 44 to therapy (15 chemotherapy, 10 radiotherapy, 5 surgery, 14 new agents). Gliomas were the most common tumor type examined, with 70 articles. There was a significant increase in the number of citation classics in the late 1990s, which was paralleled by an increase in studies examining tumor pathogenesis, chemotherapy, and new agents along with laboratory and randomized studies. CONCLUSIONS: The majority of citation classics in neuro-oncology are related to gliomas and pertain to tumor pathogenesis and treatment. The rise in citation classics in recent years investigating tumor biology, new treatment agents, and chemotherapeutics may reflect increasing scientific interest in nonsurgical treatments for CNS tumors and the need for fundamental investigations into disease processes.
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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 | 0.059 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.167 | 0.190 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".