Bizhan Aarabi and Knowledge Development in Neurotrauma
Bibliographic record
Abstract
Neurotrauma (NT) is one of the common causes of mortality and morbidity. Investigating the role of people who had an impact on the development of knowledge of NT is reasonable. Our aim is to investigate the role of Bizhan Aarabi, professor of Neurosurgery, on the knowledge development in NT. Accordingly, we searched the Scopus database for Bizhan Aarabi on August 8, 2020 and selected papers with at least 10 citations, investigating his impact on NT and details of his publications. He has published 168 papers including original articles, reviews, conference papers, letters, and editorials according to the Scopus databases. There are 112 papers with 10 or more citations. Thirty-eight out of 112 papers (33.9%) were in the first and the highest rank journal: 29 in Neurosurgery and 9 in the Journal of Neurotrauma. Twenty-four papers have the level of evidence (LOE) of "1". Bizhan Arabi developed knowledge in NT especially in the cervical spine/spinal cord trauma and brain injury and his publications are references for spine/neurosurgeons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".