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Record W3154050767 · doi:10.34172/aim.2021.36

Bizhan Aarabi and Knowledge Development in Neurotrauma

2021· article· en· W3154050767 on OpenAlexaff
Zahra Ghodsi, Shahriar Ghashghaei, Masoud Sohrabi, Mohammad Hosein Ranjbar Hameghavandi, Hossein Rezaei Aliabadi, Ahmad Pour‐Rashidi, Mahkameh Abbaszadeh, Seyed Mohammad Ghodsi, Vafa Rahimi‐Movaghar

Bibliographic record

VenueArchives of Iranian Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Toronto
FundersTehran University of Medical Sciences and Health Services
KeywordsScopusNeurosurgeryMedicineWeb of scienceMEDLINESpinal cord injuryMeta-analysisSurgeryPsychiatrySpinal cordInternal medicinePolitical science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.375
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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