Establishing a core outcome set for blunt cerebrovascular injury: an EAST modified Delphi method consensus study
Bibliographic record
Abstract
Objectives: Our understanding of blunt cerebrovascular injury (BCVI) has changed significantly in recent decades, resulting in a heterogeneous description of diagnosis, treatment, and outcomes in the literature which is not suitable for data pooling. Therefore, we endeavored to develop a core outcome set (COS) to help guide future BCVI research and overcome the challenge of heterogeneous outcomes reporting. Methods: After a review of landmark BCVI publications, content experts were invited to participate in a modified Delphi study. For round 1, participants submitted a list of proposed core outcomes. In subsequent rounds, panelists used a 9-point Likert scale to score the proposed outcomes for importance. Core outcomes consensus was defined as >70% of scores receiving 7 to 9 and <15% of scores receiving 1 to 3. Feedback and aggregate data were shared between rounds, and four rounds of deliberation were performed to re-evaluate the variables not achieving predefined consensus criteria. Results: From an initial panel of 15 experts, 12 (80%) completed all rounds. A total of 22 items were considered, with 9 items achieving consensus for inclusion as core outcomes: incidence of postadmission symptom onset, overall stroke incidence, stroke incidence stratified by type and by treatment category, stroke incidence prior to treatment initiation, time to stroke, overall mortality, bleeding complications, and injury progression on radiographic follow-up. The panel further identified four non-outcome items of high importance for reporting: time to BCVI diagnosis, use of standardized screening tool, duration of treatment, and type of therapy used. Conclusion: Through a well-accepted iterative survey consensus process, content experts have defined a COS to guide future research on BCVI. This COS will be a valuable tool for researchers seeking to perform new BCVI research and will allow future projects to generate data suitable for pooled statistical analysis with enhanced statistical power. Level of evidence: Level IV.
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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 | 0.263 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".