Bibliographic record
Abstract
At the 2023 Society of Neurological Surgeons meeting in Dallas, I heard post-graduate year 3 resident Remi Wilson give a talk about diversity, inclusion, and underserved populations. She made me think about the concept of “belonging,” a term representing the incorporation of the best elements of inclusion, especially as we focus and work toward promoting equity and diversity in our authors, reviewers, editors, and readers. I remembered when my first publication was accepted in a major neurosurgical journal. It was 1986 and it was the Journal of Neurosurgery. That acceptance letter said to me that “I belonged.” What I had written was believed by the field to be worthy of inclusion, worthy of reading by serious neurosurgeons, and worthy of adding to the archived foundation of our specialty. I could not yet say that these were “my peers” as I was only a junior resident, which made it even more special. I was being welcomed in. I belonged. It was such a wonderful emotion. I really could not get enough of it. I was growing year to year up the residency ladder, travelling to meetings both local and nationally, speaking to my new community, and soon having to think about a permanent position as a faculty neurosurgeon. What would I offer? To which subspecialty community would I want to join? In what cities would there be opportunities for fellowship or a permanent job? Where did I want to belong? Would I belong when I got there? Of course, as a Caucasian male, my path was a familiar one and I could see countless examples of others who had succeeded along such a route. Two of my coresidents were women, Beverly Walters and Anita North, who were the second and third women either completing or in-training at the University of Toronto when I started in 1985. They each were older than I, and both had participated in advanced postgraduate studies and had life experiences far beyond my meager few years after a quick college and medical school education. Unlike mine, their paths were not routine. Dr Walters was recently honored as the Schneider Lecturer at the 2023 meeting of the American Association of Neurological Surgeons. Back in residency, I wonder if these 2 women felt like they “belonged.” I do know that some of the faculty were especially tough on them and I saw that from my own vantage point. Every training program is working to become more diverse, and the medical evidence indicates that patients are often served better if their physician “looks like them,” “sounds like them,” or personally understands their heritage.1,2 Sometimes this reflects medical outcomes, and sometimes practice dynamics as simple as keeping doctors' appointments and participating in preventive medicine. Many reports in this journal and others provide information on this changing landscape, identifying potential barriers, charting the pace of progress, and sometimes providing meaningful solutions. “Looks like them” goes both ways of course. We all look for role models and are inspired in a special way when we see someone who had a path with which we can identify. That is why promotion of gender and racial diversity in our leaders, as well as internationalism, are powerful tools that first help people become interested in neurosurgery and then realize that they can and do belong. So how does this pertain to a medical journal? First, we promote diversity in our editorial board across many domains to be representative. This includes internationalism, gender, age, and racial participation. Our Resident Publications Fellows are but one example of this; our current Fellows Drs Ali Alawieh and Alexandra Giantini Larsen participate in and lead article peer-review, attend biweekly staff meetings, and work on unique projects. We attract and publish articles on topics related to diversity because it pertains to becoming and being a neurosurgeon. As noted above, this can affect clinical outcomes. Many focus on understanding the neurosurgical work force, both in training and in practice. Some submissions are rather straightforward and present data from different sources on the current state of affairs. But much of this information is known and appreciated, and some do not provide new insights. The best articles, like any scientific report, ask an interesting question, collect meaningful and robust data, provide useful recommendations, and bring something new to the reader. These last 2 elements are typically lacking from articles that do not get accepted. Fundamentally, we all succeed when we foster a sense of belonging, and the yield is high. In 1 instance, our ideas belong when they are accepted for publication in a major journal. That was personally gratifying for me and a major driver in my career development. Douglas Kondziolka, MD, MSc Editor-in-Chief, Neurosurgery Publications New York, New York, USA
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.165 | 0.065 |
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".