Neurointerventional surveys between 2000 and 2023: a systematic review
Bibliographic record
Abstract
BACKGROUND: Surveys are increasingly used in neurointervention to gauge physicians' and patients' attitudes, practice patterns, and 'real-world' treatment strategies, particularly in conditions for which few, or no evidence-based, recommendations exist. While survey-based studies can provide valuable insights into real-world problems and management strategies, there is an inherent risk of bias. OBJECTIVE: To assess key themes, sample characteristics, response metrics, and report frequencies of quality indicators of neurointerventional surveys. METHODS: A systematic review compliant with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was performed. The PubMed database was searched for neurointerventional surveys published between 2000 and 2023. Survey topics, design, respondent characteristics, and survey quality criteria suggested by the Consensus-Based Checklist for Reporting of Survey Studies (CROSS) were assessed and described using descriptive statistics. Response rates and numbers of participants were further assessed for their dependence on sample characteristics and survey methodologies. RESULTS: A total of 122 surveys were included in this analysis. The number of surveys published each year increased steeply between 2000 (n=1) and 2023 (n=14). The most common survey topics were stroke (51/122, 41.8%) and aneurysm treatment (49/122, 40.2%). The median response rate was 58.5% (IQR=30.4-86.3), with a median number of respondents of 79 (IQR=50-201). Sixty-eight of 122 (55.7%) surveys published the questionnaire used for data collection. Only a subset of studies reported response rates (n=89, 73%), data collection time period (n=91, 74.6%), and strategies to prevent duplicate responses (n=57, 46.7%). CONCLUSION: Surveys are increasingly used by neurointerventional researchers, particularly to assess real-world practice patterns in endovascular stroke and aneurysm treatment. Adapting best-practice guidelines like the CROSS checklist might improve homogeneity and quality in neurointerventional survey research.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".