Research experience, goals, and priorities of pediatric neurosurgeons: a survey of the American Society of Pediatric Neurosurgeons
Bibliographic record
Abstract
OBJECTIVE: Neurosurgeon scientists play a unique role in advancing neuroscience research. While previous publications have explored trends in federal and foundation funding among neurosurgeons, funding is often dominated by neurosurgical oncologists and functional neurosurgeons. Less is known about the research efforts of pediatric neurosurgeons. The aim of this study was to survey the members of the American Society of Pediatric Neurosurgeons (ASPN) to provide an overview of past research experience, current involvement, funding, and research priorities among pediatric neurosurgeons, and to gather insights that could shape future efforts to advance pediatric neurosurgical research. METHODS: A survey was developed using the REDCap platform and distributed to all ASPN members via email. Survey questions used branching logic and were organized into 5 sections: 1) demographics, 2) research experience during training, 3) research experience as an attending physician, 4) research priorities, and 5) multicenter consortiums. RESULTS: One hundred thirty-nine respondents completed more than half of the survey, for an overall response rate of 52.1%. Most respondents (96.4%) participated in research during their training, but only 38.1% had received a grant during training. In contrast, 83.9% of respondents were actively engaged in research as an attending physician, and 48.7% reported active funding (60.7% federal, 41.8% from foundations, and 42.9% internal). Furthermore, 74.8% of respondents reported being a member of a multicenter research consortium, and 82.4% agreed that multicenter research is important. Seventy percent of respondents agreed that the ASPN should facilitate multicenter consortium-based pediatric neurosurgical research, offering free-text responses with the following suggestions: 1) set aside time at the annual meeting to discuss multicenter research (22.9%); 2) encourage collaboration and facilitate networking (42.9%); 3) provide centralized core services such as a data coordinator and biostatistician (12.9%); and 4) provide training, education, and mentoring (7.1%). CONCLUSIONS: The survey provided a cross-sectional analysis of the pediatric neurosurgical research landscape, highlighting the current state of research experience, funding, and the perspectives of pediatric neurosurgeons regarding research priorities. Despite the challenges, there is clear recognition of the importance of multicenter research collaboration. These findings reinforce the ongoing necessity of organized initiatives to support pediatric neurosurgical research and offer actionable insights into how organized pediatric neurosurgery can contribute to this critical endeavor.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".