P.128 Evaluation of endovascular access complexity on stroke patient’s initial imaging: an agreement study
Bibliographic record
Abstract
Background: Neck vessel imaging is often performed in hyperacute stroke to allow neurointerventionalists to estimate access complexity. This study aimed to assess clinician agreement on catheterization strategies based on imaging in these scenarios. Methods: An electronic portfolio of 60 patients with acute ischemic stroke was sent to 53 clinicians. Respondents were asked: (1) the difficulty of catheterization through femoral access with a regular Vertebral catheter, (2) whether to use a Simmons or reverse-curve catheter initially, and (3) whether to consider an alternative access site. Agreement was assessed using Fleiss’ Kappa statistics. Results: Twenty-two respondents (7 neurologists, 15 neuroradiologists) completed the survey. Overall there was slight interrater agreement (κ=0.17, 95% CI: 0.10–0.25). Clinicians with >50 cases annually had better agreement (κ=0.22) for all questions than those with fewer cases (κ=0.07). Agreement did not significantly differ by imaging modality: CTA (κ=0.18) and MRA (κ=0.14). In 40/59 cases (67.80%), at least 25% of clinicians disagreed on whether to use a Simmons or reverse-curve catheter initially. Conclusions: Agreement on catheterization strategies remains fair at best. Our results suggest that visual assessment of pre-procedural vessels imaging is not reliable for the estimation of endovascular access complexity.
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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 | 0.026 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".