P.134 Neurosurgery research output in The Association of Southeast Asian Nations (ASEAN) region: a scientometric analysis
Bibliographic record
Abstract
Background: Various challenges and innovations have led to the evolution of neurosurgery in the ASEAN region. This has increased interest among neurosurgeons to publish research papers for the past years. The study aims to compare the publication trend, and topic trend on research in the region using scientometric techniques. Methods: Publications from Web of Science (WoS) using the keywords “neurosurgery” OR “neurological surgery.” were obtained. Results only included English articles published from ASEAN countries. Publication, citation, collaboration, and text-co-occurrence analysis were done using WoS and VOSViewer. Results: 1951 articles published between 1996 to 2022 were analyzed. The ASEAN countries’ productivity are: Singapore (34.07%), Thailand (21.66%), Indonesia (15.25%), Malaysia (14.72%), Philippines (5.99%), Vietnam (5.15%), Cambodia (1.78%), Myanmar (1.16%), Brunei (0.21%). Singapore, Thailand, Malaysia, and Indonesia were the top research collaborators. Publications have clusters of co-occurring keywords: (1) seizure, aneurysm, pain; (2) traumatic brain injury, mortality, functional outcome; (3) technology, application; (4) survey, training; (5) glioblastoma, brain metastases, chemotherapy. Conclusions: Trend in publications support the growing importance of neurosurgery. Variations in publications are attributed to differences in research interest, training, technology and culture between countries. These are relevant to aid in future capacity-building projects, research agendas, policy guidelines, and collaboration between countries, to improve research production.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.059 | 0.112 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".