Challenges and opportunities in research funding for neurovascular diseases from a clinical researcher's perspective
Bibliographic record
Abstract
BACKGROUND & PURPOSE: Neurovascular research is underfunded, imposing substantial challenges on clinical researchers in the field of neurovascular diseases. We explored what physicians perceive to be the greatest challenges with regard to neurovascular research funding, and how they think the funding crisis in neurovascular research could be overcome. METHODS: We performed an international, multi-disciplinary survey among physicians involved in the medical care of patients with neurovascular diseases. After providing their demographic data, physicians were asked closed-ended questions on their personal opinion regarding challenges in neurovascular research funding, and how these challenges could be overcome. Physicians also described in their own words what they perceived to be the biggest challenges in obtaining funding. Data were analyzed using descriptive statistics and response clustering. RESULTS: Of 233 participating physicians (70.4% male,82.8% senior staff) from 48 countries, 217(97.4%) perceived the discrepancy between required and available funding to be a problem;172(73.8%) considered it a major problem. High competitiveness (61/118 available free text responses[51.7%]), time-consuming application processes (28/118[23.7%]) and administrative requirements (25/118[21.1%]) were identified as key obstacles. Traditional big funding agencies were perceived to be most capable of closing the neurovascular research funding gap, followed by specialty-specific organizations and industry, while philanthropy and crowdfunding were perceived to be less important. CONCLUSION: The gap between required and available funding was perceived to be a major problem in neurovascular research, with high competitiveness, time-consuming funding processes and excessive administrative requirements being the key obstacles to obtaining funding. Traditional funding agencies were perceived to be most capable of closing this funding gap.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".