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Record W3177301262 · doi:10.51437/jgns.v1i1.29

A Seat at the Table: Representation of Global Neurosurgery in the G4 Alliance

2021· article· en· W3177301262 on OpenAlexaff
Ulrick Sidney Kanmounye, Natalie Shenaman, Marj Ratel, Kee B. Park, Sarah Woodrow, Comrade Lawal-Aiyedun, Suzanne Tharin, Tariq Khan, Makinah Haq, Elliott Taylor, William Harkness, Nathan A. Shlobin, Richard Moser, Josh Korn, Robert J. Dempsey, Gail Rosseau

Bibliographic record

VenueJOURNAL OF GLOBAL NEUROSURGERY · 2021
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsKorle Bu Neuroscience Foundation
Fundersnot available
KeywordsAllianceGrassrootsOutreachPublic relationsCivil societyGlobal healthPolitical scienceHealth careMedicineBusinessLaw

Abstract

fetched live from OpenAlex

The Global Alliance for Surgical, Obstetric, Trauma and Anaesthesia Care (G4 Alliance, http://www.theg4alliance.org/) is thepreeminent global surgery advocacy organization (1) dedicated to eliminating disparities in surgical care around the world. A coalitionof over 60 non-profit organizations, professional societies, academic centers, and other groups, the G4 Alliance represents civil societyinterests in global surgery from the grassroots to the international level. The organization convenes its membership to address commoninterests and concerns, facilitates knowledge and resource sharing, builds consensus and alignment, and conducts outreach withmultilateral organizations, donors, and other global health programs and advocacy initiatives. In a sector characterized by diverseactors and fragmentation of care delivery, scholarship, and funding (2,3), the G4 Alliance provides communication and coordinationaround a common surgical and health system strengthening agenda. The purpose of this paper is to highlight the multiple organizationsthat specifically contribute to neurosurgical advocacy within the G4 Alliance.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
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 head0.030
GPT teacher head0.327
Teacher spread0.297 · 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

Citations18
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJOURNAL OF GLOBAL NEUROSURGERYSame topicGlobal Health and SurgeryFrench-language works237,207