Equity in Global Neurosurgery Publications: Breaking Down Barriers in Discourse
Bibliographic record
Abstract
As neurosurgery steps into a new era of global collaboration in clinical care and teaching, neurosurgeons' academic opportuni ties inlower resource countries lag their counterparts in higher-income countries (1). Recent efforts to quantify and bring attention to the gapin surgical care globally with the Lancet Commission have trickled into neurosurgery, demonstrating the challenges in low res ourceclinical settings and their lack of representation in the literature (2). Our focus is often and most readily drawn to these disparities incare – but that is only part of the healthcare divide. The inequities that exist in neurosurgery extend beyond the operating room andinto academia. We are confident that this inaugural issue of the Journal of Global Neurosurgery represents a meaningful step towardsimproving access to academia's traditionally rarefied world for all interested neurosurgery providers.Halting the perpetuation of this historical "north-south gap" requires a conscious effort in our community to equalize opportunities. In2020 alone, over 80 new publications were indexed on PubMed with the keyword "global neurosurgery" – twice as many as 2018 and2019. Even with a steadily increasing interest in this domain, most reports in global neurosurgery – both in terms of impact andquantity – originate from high-income countries (3). Young neurosurgeons in low- and middle-income countries have universallypointed to a lack of research opportunities as the most common personal challenge to their practices, in addition to limited access tomentors, journals, and textbooks (1). There is a growing audience for global neurosurgical literature and a documented interest amongyoung neurosurgeons for science access.
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 | 0.229 | 0.476 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.034 | 0.027 |
| Science and technology studies | 0.014 | 0.040 |
| Scholarly communication | 0.051 | 0.084 |
| Open science | 0.004 | 0.049 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 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".